Various methods of machine learning that can be explained and examples of implementations

Explainable Machine Learning

Explainable Machine Learning (EML) refers to methods and approaches that explain the predictions and decision-making results of machine learning models in an understandable way.

In many real-world tasks, model explainability is often important. This can be seen, for example, in solutions for finance, where it is necessary to explain which factors the model is basing its credit score on, or in solutions for medical diagnostics, where it is important to explain the basis or reason for its predictions for a patient.

In contrast, traditional machine learning models are a complex black box with a large number of parameters, making it difficult to explain how the model derived its results.

Explainable machine learning methods have been developed to address these challenges and clarify the reasons for model predictions and decisions, including

• Feature Importance Interpretation: This would be a way to explicitly indicate which features the model is using to make predictions or decisions.
  • In tree-based models, such as random forests and gradient boosting, the importance of each feature can be calculated and indicated.
  • In linear models, the coefficients of features can be interpreted to identify important features.
• Local Interpretability: for a particular instance, this would be a way to explain why the model made the prediction or decision it did.
  • LIME (Local Interpretable Model-Agnostic Explanations) is a method for explaining the predictions of a particular instance, which uses neighborhood data to approximate the local behavior of the model with an interpretable model.
  • SHAP (SHapley Additive exPlanations) is a method that applies the Shapley value concept of game theory to machine learning, allowing the evaluation of feature contributions.
• Model Visualization: This method visualizes the internal structure of the model and the decision-making process.
  • In the case of decision trees and decision tree-based ensemble models (random forests and gradient boosting), the tree structure can be visualized, which facilitates understanding the decision-making process of the model.
  • In the case of neural networks, visualization of the activation of the intermediate layers makes it possible to understand which features of the input data the model is focusing on. For example, Grad-CAM (Gradient-weighted Class Activation Mapping) can be a method to visualize regions of interest for image recognition models.
• Generation of rules and explanatory text: This is a method for explaining model predictions and decisions in terms of rules and natural language.
  • Rule-based methods generate the rules used by the model to make predictions and decisions, thereby providing a clear explanation based on conditions and rules.
  • Natural language generative models may be used to generate explanatory text for predictions. This allows the model’s predictions to be explained in a way that is easily understood by humans.

Furthermore, a combination of these methods can be used to build an explainable machine learning model. Explainability is important not only for improving user confidence and model fairness, but also for diagnosing errors and providing suggestions for improvement.

On the algorithms used in machine learning that can be explained

Various algorithms are used in explainable machine learning, including

• Linear Regression:

Linear regression models express predictions as a weighted sum of features. It models the dependence of a feature x on an objective variable y. For more information, see Explaining Machine Learning (1) Interpretable Models (Linear Regression Models).

• Logistic Regression:

While the linear regression model is an algorithm that minimizes the distance by fitting a straight line or hyperplane to classify, the logistic regression model uses a logistic function to transform the output of a linear equation between 0 and 1 (transforming it into a probability). For details, see “Explainable Machine Learning (2) Interpretable Model (Logistic Regression Model).

• Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs):

GLMs and GAMs address the problem of linear models where the true relationship between the features and the result is non-linear. This is the case when the features are categories or biased outputs for which there are a small number of very large numbers, such as time until machine failure, or when a normal linear model gives the same effect on the predicted results whenever the value is increased by 1, for example, when the temperature increases from 10 to 11 degrees Celsius, or from 40 to 41 degrees Celsius. For example, a rise in temperature from 10 to 11 degrees Celsius and a rise in temperature from 40 to 41 degrees Celsius will have different effects on the predicted results. For details, please refer to “Explainable Machine Learning (3) Interpretable Models (GLM, GAM).

• Decision Trees:

Similar to GLMs and GAMs, decision trees, decision rules, random forests, RuleFit, etc. can be used when the true relationship between features and results is nonlinear. Tree-based models divide the data multiple times based on some cutoff value for the features, and through this division, the data set is divided into different subsets. Each instance will belong to one of these subsets. The last subset is called the terminal node or leaf node, and the middle subset is called the internal node or split node. Decision trees are easy to understand visually and help us understand the importance of features and decision paths. For details, please refer to “Explainable Machine Learning (4) Interpretable Model (Decision Tree).

• Decision Rule:

A decision rule is a simple IF-THEN statement consisting of a condition (also called an assumption) and a prediction. Typical algorithms include oneR, Sequential covering, Bayesian Rule Lists, etc. For details, please refer to “Explainable Machine Learning (5) Interpretable Models (Decision Rules).

• Random Forest and Causal Forest:

Random Forest is an ensemble learning method as described in “Overview of Ensemble Learning and Examples of Algorithms and Implementations” that combines multiple decision trees, calculates the importance of features, and integrates the results of individual decision trees to improve explanatory power. An application of Random Forest is Causal Forest. For more information, see “Overview of Causal Forest, Application Examples, and Examples of Implementations in R and Python.

• RuleFit:

The RuleFit algorithm is used to train a sparse linear model that combines interactions between features by training a sparse linear model using the original features and a number of new features that are decision rules. The generated features are automatically generated by combining the split decisions from the decision tree into rules and converting each path through the tree into a decision rule. For details, please refer to “Explainable Machine Learning (6) Interpretable Model (RuleFit).

• Support Vector Machines:

Support Vector Machines are powerful methods for linear and nonlinear classification that find support vectors to determine optimal boundaries and interpret their relationships to data features. For more information, see “Overview of Kernel Methods and Support Vector Machines.

• Gradient Boosting and LightGBM:

Gradient boosting is a method of building predictive models by sequentially combining weak learners (usually decision trees) to highlight important features and help understand the importance of features. A typical example of gradient boosting is LightGBM. See “Overview of LightGBM and its implementation in various languages” for details.

• Partial Dependence Plot (PDP):

PDP is a method of observing the model’s prediction results by selecting one of the features and varying the values of the other features while keeping the values of the other features fixed. Specifically, the values of the selected features are varied within a certain range, and the mean value or distribution of the prediction results corresponding to the variation is plotted. The graph obtained by the partial dependence plot shows the relationship between the value of the selected feature and the prediction results, which helps to understand how important the feature is for the prediction, and also visualizes the effect of the interaction of different feature combinations. Partial dependence plots can be used in both regression and classification models, where the mean of the predictions is plotted for the regression model, and the distribution of probabilities or predictions belonging to each class is plotted for the classification model. For more information, see Explainable Machine Learning (7) Model Independent Interpretation (PDP).

• Individual Conditional Expectation (ICE) Plot:

ICE Plot is an extended version of PDP Plot and is a method to visualize how feature values affect individual observed values. Specifically, ICE Plot changes the value of a feature within a certain range for each data point, and plots the value of the prediction result corresponding to that change. Thus, the ICE Plot will have multiple curves for individual data points and will provide more detailed information than the Partial Dependency Plot. While the partial-dependence plot shows how the average value of the prediction result changes as the value of the feature changes, the ICE plot shows the change in the value of the prediction result for each individual data point, thereby allowing the user to capture different trends and features for each data point.

ICE plots can be used to visualize the impact of a feature on individual data points and, in particular, to compare differences in the impact of a feature among different data points. ICE plots can also be used to understand changes in the predicted results for a particular data point and to assess the reliability or uncertainty of the model’s predictions. See Explainable Machine Learning (8) Model Independent Interpretation (ICE Plot)” for more details.

• Accumulated Local Effects (ALE) Plots:

ALE plots, like PDP plots and ICE plots, are used to visualize the impact of changes in feature values on the model’s predicted results. (2) accumulate the local effects for each change, and (3) plot the accumulated local effects.

The graph produced by the ALE Plot shows the cumulative changes in the predicted results over a range of feature values, which allows one to understand how the feature affects the predicted results. It also provides smoother results compared to PDP Plot and ICE Plot for interpolating changes between data points to compute local effects, and is better suited for evaluating the effects of multiple features, including interaction effects and nonlinear relationships For more information on the ALE Plot, see ” Explainable Artificial Intelligence (9) Model Independent Interpretation (ALE plot)“.

• Interpreting Feature Interdependence:

Interpretation of feature interactions in predictive models involves (1) visualization using a combination of PDP plots and ALE plots (e.g., creating a PDP plot for two features and comparing the plots to understand the effect of the interaction between the features), and (2) interpretation of the coefficient of each feature in linear models and some tree-based models (interpreting the direction and strength of the interaction between features while considering the sign and relative magnitude of the coefficients), (3) generating new features from existing features to understand the interaction of features (e.g., adding the product or difference of two features as a new add them as feature values and evaluate their coefficients and importance), (4) calculate the contribution of each feature value and interpret the SHAP value (see below), which indicates how much the feature values contribute to the prediction results. For details on the interpretation of feature interactions, see “Explainable Artificial Intelligence (10) Model-Independent Interpretation (Feature Interactions).

• Permutation Feature Importance:

Permutation Feature Importance is used to evaluate the importance of a feature. The specific procedure is as follows: (1) train the model on a training set and calculate the initial prediction accuracy by evaluating the prediction accuracy on a test set, (2) select the features to be evaluated, (3) permute (reorder) the values of the selected features at random and shuffle the feature values at random (4) make predictions using the permuted data, (5) calculate the prediction accuracy using the permuted data, (6) consider the difference between the original prediction accuracy and the prediction accuracy using the permuted data as the importance of the feature, and the larger the decrease in prediction accuracy, the more important the feature is considered. The greater the decrease in prediction accuracy, the more important the feature is considered to be. Since each feature is evaluated independently, this method also takes into account interactions among features and allows quantification of the contribution of features to the model’s prediction performance. For details on Permutation Feature Importance, please refer to “Explainable Artificial Intelligence (11) Model-Independent Interpretation (Permutation Feature Importance).

• Global Surrogate Model: (Global Surrogate Model):

The global surrogate model replaces the original predictive model and builds a more interpretable alternative model (surrogate model). The specific steps are (1) train the original prediction model using a complex model (e.g., deep learning model) or a model that is considered a black box model, (2) use the training data of the original model to obtain prediction results, and (3) use the training data of the surrogate model (4) train an interpretable model (e.g., linear model or decision tree model) to approximate the predictions of the original model, and (5) interpret the importance, coefficients, etc. of the features of the surrogate model in order to attempt to understand the predicted results.

The advantage of the global surrogate model is that it is more interpretable than the original model, and because it uses an interpretable model, it is easier to intuitively understand the importance and impact of the features. Another advantage is that if the surrogate model itself is lightweight and fast, it can quickly approximate the predictions of the original model. For more information on global surrogate models, see Explaining Artificial Intelligence (12) Model-Independent Interpretation (Global Surrogate).

• LIME (Local Interpretable Model-Agnostic Explanations):

LIME builds an interpretable model for a specific data point that approximates its predicted outcome. The specific steps are (1) select the data points to be interpreted, (2) generate data points in the neighborhood of the data points by randomly changing the values of the features, (3) input the generated data points to the original model and obtain the prediction results, and (4) use the generated data points and their predicted results are used to train an interpretable model (e.g., linear model or decision tree model), (5) interpret the importance, coefficients, etc. of the features of the interpretable model to understand the predicted results of the original model.

The purpose of LIME is to make black box model predictions interpretable for individual data points, and by generating neighborhood data points, the interpretable model can approximate the behavior of the model in the region surrounding the data points, making it model-independent and applicable to various types of prediction model-independent and applicable to various types of forecasting models.

LIME provides interpretability for individual data points, which is especially useful when interpreting model predictions that are surprising or outliers. For more information on LIME, see “Explainable Artificial Intelligence (13) Model Independent Interpretation (Local Surrogate :LIME).

• Scoped Rules or Anchors:

Scoped Rules or Anchors allow for the understanding of prediction results by generating concise and interpretable rules for individual data instances. The specific steps are (1) select the data instances to be interpreted, (2) generate rules to provide an explanation for the prediction results applicable to the instances (the rules are expressed in the form of a range of specific features or conditions), and (3) the rules are optimized to balance interpretability and explanation of the prediction results. optimized to achieve a balance. To find the right balance, Scoped Rules may constrain the scope (coverage) of the rule, where scope controls the trade-off between accuracy and brevity of the explanation; (4) the generated Scoped Rules are used to interpret the predicted results of the data instance. If a rule is satisfied, the predicted result is explained based on that rule.

The purpose of Scoped Rules would be to explain the prediction results with interpretable rules for each individual data instance. Scoped Rules are particularly useful for explaining model predictions in sensitive domains, such as medical diagnostics and credit scoring, where certain attributes or conditions have a significant impact on predictions. For more information on Scoped Rules, see “Explainable Artificial Intelligence (14) Model-Independent Interpretation (Scoped Rules (Anchors)).

• Sharpley value:

Sharpley value is a method for evaluating the importance of features derived from game theory as described in “Overview of Game Theory, Integration with AI Technology, and Implementation Examples,” and is used to quantify how much a feature contributes to the predictive outcome of a model.

The concept of Sharpe Ray value is based on the distribution of gains in a cooperative game. In a cooperative game, players form teams and cooperate toward a team goal, and the Sharpenray value evaluates the contribution of each player to the gains brought about by the cooperation.

The calculation of the Sharpray value of a feature is performed by the following procedure. (1) calculate the prediction results for all feature combinations while varying the degree of participation of each feature (i.e., using randomly shuffled data for each feature value), (2) calculate the difference of the prediction results for each feature combination, and (3) calculate the average of the difference of the prediction results for all feature The average of the differences in the prediction results for the combination is used as the Sharpeley value of the feature.

The Sharpe Ray value is used to evaluate the importance of a feature, and a feature with a higher Sharpe Ray value is considered to have a greater impact on the model’s predictions (especially useful when multiple features are interacting or when a combination of features is important). Sharpe Ray values are also a general-purpose method that is model-independent and can be applied to any type of model, and Sharpe Ray values can also be used to rank and compare features. However, since the number of feature combinations increases exponentially, it can be difficult to compute when the number of features is large, and approximate methods and efficient algorithms are those that have been proposed. For details on sharpley value, see “Explainable Artificial Intelligence (15) Model-Independent Interpretation (Sharpley Value).

• SHAP (SHapley Additive exPlanations):

SHAP is an application of Shapley values to model interpretation and is an alternative estimation method to kernel-based Sharpley values inspired by local surrogate models.

The SHAP approach calculates the Sharpe Ray value of each feature and quantifies the contribution of the feature to the model’s predicted results. The SHAP value is uniquely defined for each feature, which evaluates the individual contribution of each feature and, furthermore, the amount of information needed to generate the model’s predicted results (the more information a feature provides, the larger the SHAP value for that feature). The sum of the SHAP values of all the features is calculated to match the prediction results, and the contribution of the features to the overall prediction results is also retained.

SHAP values are used extensively to enhance the interpretability of the model and are used to (1) evaluate the importance of individual features (features with higher SHAP values are considered to have a greater impact in the model’s predictions), (2) evaluate how much each feature contributes to a particular prediction outcome (i.e., how much of the (3) it is also used to evaluate interactions and dependencies between features (by evaluating the SHAP value for a combination of features, the influence of the interaction can be elucidated). values, see “Explainable Artificial Intelligence (16) Model-independent Interpretations (SHAP (SHapley Additive exPlanations)“.

• Interpretation with Counterfactual Explanations:

Counterfactual explanations provide a way to change the predicted outcome of an instance by changing the value of a particular feature when the predicted outcome is different.

Counterfactual Explanations include (1) Counterfactual Explanations, which is a method of changing the value of a particular feature to reverse the predicted outcome of an instance (e.g., if the model predicts that “this image is a dog,” Counterfactual Explanations would change the prediction to “this image is a dog”), and (2) Counterfactual Explanations would change the prediction to “this image is a dog,” Counterfactual Explanations would change the prediction to “this image is a dog” and change the value of a particular feature to reverse the prediction. Counterfactual Explanations show the change of a feature value to predict “this image is not a dog”), and (2) Counterfactual Perturbation, a method of counterfactual explanation that perturbs the value of a particular feature to change the predicted result of an instance (Counterfactual Perturbation is a method of perturbing the value of a feature to change the predicted result of an instance). Perturbation is done by changing the value of a feature in small increments, which produces instances near the boundary where the predicted outcome changes, allowing us to evaluate the reliability or uncertainty of the model’s prediction), (3) Counterfactual Perturbation, one of the counterfactual explanations, which not only changes a specific feature value to change the predicted outcome, but also compares it to other classes or another outcome in order to change the predicted outcome, and (3) Contrastive Explanations (which allow us to understand why a particular class or outcome was chosen).

Counterfactual explanations provide a basis for model confidence and explanation by explicitly indicating how the model’s predictions may change when interpreting the model’s results, and counterfactual explanations may also provide an interactive means of interpretation for the user. It should be noted, however, that counterfactual explanations may involve the cost of building and computing an explainable model, and the difficulty of interpretation may vary depending on the number of features and the complexity of the data. For details on counterfactual explanations, see “Explainable Machine Learning (17) Counterfactual Explanations.

• Adversarial Samples Interpretation:

Adversarial sample interpretation is a method for evaluating model predictions and their interpretability. Adversarial samples are samples from which model predictions can be changed by adding small perturbations to the input data.

Interpretation by hostile samples is used for the following purposes. (1) assess the instability of the model (the hostile sample is used to assess how sensitive the model is to small changes, and a model is considered unstable if small changes in the input data significantly alter the predictions of the model), (2) assess the robustness of the interpretation ( Hostile samples are also used in evaluating the importance of interpretable models and features, and an interpretation is considered not robust if the interpretation results change significantly due to hostile samples), (3) evaluating the importance of features (hostile samples are also used as one method to evaluate the importance of features, and hostile samples are also used as one method to evaluate the importance of features), (4) evaluating the robustness of interpretation (hostile samples are also used as one method to evaluate the robustness of features), (5) evaluating the importance of features (hostile samples are also used as one method to evaluate the robustness of features) (Adversarial samples are also used as one method to evaluate the importance of a feature, and the importance of a feature can be quantified by generating a hostile sample and evaluating the change in prediction results for each feature).

Interpretation methods using hostile samples are useful for evaluating model reliability and interpretation stability, and optimization algorithms and specific constraints (e.g., perturbation constraints) are commonly used to generate hostile samples. It should be noted, however, that the reliability of interpretation by adversarial samples depends on the size and complexity of the perturbations generated, and the optimal adversarial sample methodology may vary depending on the interpretation objective and domain. For more information on Adversarial Examples, see Explaining Machine Learning (18)Adversarial Examples.

• Interpretation by proto type and criticism:

A prototype-based interpretation method selects representative data points (prototypes) in a dataset and evaluates the importance of these features. Typically, prototypes are associated with each class or specific prediction outcome, and by analyzing the relationship between the feature values and importance of the prototypes, it is possible to evaluate the contribution and importance of the features.

Criticism-based interpretation methods identify data points (criticisms) that question or criticize the model’s predicted results and evaluate the importance of those features. Criticisms are data points for which the model’s predicted results may be incorrect, and by analyzing the relationship between the value and importance of the feature values of Criticism, the contribution and importance of the feature can be evaluated.

Because Prototype and Criticism provide interpretation at the data point level, it is possible to focus on individual forecast results or specific data points, which allows one to understand how the model’s forecast results are derived and how much a particular data point influences the forecast Prototype- and Criticism-based methods are particularly useful for interpreting erroneous or anomalous model predictions. For details on Prototype and Criticism, see “Explainable Machine Learning (19)Prototype and Criticism.

Libraries and platforms used for machine learning that can be explained

The following libraries and platforms are widely used for explainable machine learning

• Scikit-learn: Scikit-learn is an open source library for machine learning used in Python. computation, and local interpretability methods (e.g., LIME).
• XGBoost: XGBoost is a fast and effective library that implements the gradient boosting algorithm. XGBoost supports feature importance calculations and model visualization to improve model interpretability.
• ELI5: ELI5 (Explain Like I’m 5) will be a library for explaining the predictive results of machine learning models; ELI5 provides various methods for calculating feature importance, partial dependency plots, and model visualization.
• There are many R packages that implement PDP. There is the iml package, as well as pdp and DALEX. In Python, partial dependence plots are implemented by default in scikit-learn, and PDPBox is also available.
• ICE plots are implemented in iml (used in these examples), ICEbox, and the R package of pdp. Another R package very similar to ICE would be condvis.
• The iml package for R is available from CRAN for the current version and from Github for the development version. There are other implementations for specific models. The R package pre implements RuleFit and the H statistic. The R package gbm implements the gradient boosting model and the H statistic.
• The R package iml is available as an implementation of Permutation Feature Importance. The R packages DALEX and vip and the Python library alibi also have model-independent implementations of permutation feature importance.
• Surrogate models can use the R package iml for the actual implementation. If you can learn machine learning models, you can implement surrogate models yourself simply by learning interpretable models that predict the predictions of black box models.
• LIME is implemented in Python (lime library) and R (lime package and iml package) and is very easy to use.
• For Anchor, two implementations are currently available: anchor in the Python package (also integrated in Alibi) and Java implementation.
• In R, Sharpray values are implemented in the iml and fastshap packages. SHAP, an alternative estimation method for Sharpe ray values, will be discussed next. The other method, called breakDown, is implemented in R in the package breakDown.
• The authors of the SHAP paper have implemented shap in the Python package of SHAP. This implementation works for decision tree-based models in scikit-learn, a machine learning library for Python. shap is integrated into xgboost and LightGBM, a tree boosting framework. In R, there are shapper and fastshap packages. SHAP is also included in the xgboost package in R.
• The Python implementation of counterfactual explanations is the Alibi package.
• The MMD-critic implementations used for Prototype and Criticism are found below.

These tools implement explainable machine learning methods and provide useful features to improve model interpretability, and the major machine learning frameworks (e.g., TensorFlow, PyTorch) also provide features and extensions that support explainability.

Explainable machine learning applications

Explainable machine learning has been applied to a variety of real-world problems. The following are examples of those applications.

• Financial Sector:
  • Credit scoring: Explanatory machine learning can be used to predict credit scores for individuals and companies. The explanatory nature of the model allows understanding and explaining to borrowers and investors which factors affected the score.
  • Fraud detection: Explainable machine learning may be used to detect fraud and abuse. Explaining why and what features the model determined to be fraudulent is expected to increase confidence.
• Medical Diagnostics:
  • Diagnostic Imaging: Explanatory machine learning could be used to interpret medical images such as X-rays, MRIs, CT scans, etc., to support physician decision making by explaining which features the model based its diagnosis on.
  • Disease prediction: Using patient data and test results as input, explainable machine learning can be used to risk and predict disease, and the explainability of the model can help understand risk factors and important features and provide appropriate advice to the patient.
• Automated Driving:
  • Automotive decision making: automated driving systems can use explainable machine learning to make automotive decisions based on road conditions, and the explanatory nature of the model can improve safety and reliability by explaining how the model made its decisions.
  • Crash prediction: Explainable machine learning can be used to predict the risk of collisions with other vehicles and pedestrians, and the explainability of the model can help understand the factors and characteristics associated with crash risk and help prevent accidents.
• Online advertising:
  • Recommendations: Explainable machine learning can be used to provide personalized advertising and recommendations to users, and the explainability of the model can explain recommendations based on user preferences and characteristics to increase user satisfaction.

Explainability is an important feature not only for increasing the reliability and transparency of the model, but also for diagnosing errors and obtaining suggestions for improvement.

The following is an example of a specific implementation of explainability.

Example python implementation of credit scoring in finance using explainable machine learning

As an example of an implementation of credit scoring in the financial sector using explainable machine learning, the following is a general procedure in Python.

1. Data Preparation: Collect and preprocess customer data required for credit scoring (e.g., income, amount borrowed, past payment history, etc.). Data may include credit risk classes (e.g., 1 = credit risk, 0 = no credit risk).
2. Feature engineering: extract useful features from the data and convert them to the appropriate format. For example, it may involve encoding categorical variables, normalizing numerical variables, and calculating certain statistics (e.g., mean, standard deviation, etc.).
3. Model selection: select an explanatory machine learning model. Models such as logistic regression and decision trees are suitable for assessing the contribution of features and increasing the interpretability of credit scores.
4. Train the model: train the selected model using the dataset. Training may involve splitting the model into training and test sets, cross-validating, and adjusting parameters.
5. Interpreting the model: Interpret the trained model to evaluate the determinants of credit scores and the importance of features. For example, the coefficients of features and the rules of the decision tree can be checked to understand their contribution to the credit score.
6. Credit Scoring: Predict credit scores based on the interpretation results, using new customer data as input. Provide transparency to financial institutions and credit agencies by explaining which features and rules the model calculated credit scores based on.

The following is a simple example implementation using logistic regression with scikit-learn.

from sklearn.linear_model import LogisticRegression

# Data Preparation and Preprocessing
X = ...  # Feature vector of customer data
y = ...  # Credit risk class labels (e.g., 1=with credit risk, 0=without credit risk）

# Model Selection and Learning
model = LogisticRegression()
model.fit(X, y)

# Assessing the importance of features
importances = model.coef_[0]

# credit scoring
customer_data = ...  # New customer data
credit_score = model.predict_proba(customer_data)[:, 1]

# Indication of feature importance
for i, importance in enumerate(importances):
    print(f"Feature {i+1}: Importance={importance}")

# View Credit Scores
print("Credit Score:", credit_score)

In this example, logistic regression is used to perform credit scoring and display the importance of features. The model is trained on a dataset and then uses new customer data as input to predict credit scores.

Example python implementation of fraud detection in the financial sector using explainable machine learning

As an example of an implementation of fraud detection in the financial sector using explainable machine learning, the following is a general procedure in Python.

1. Data preparation: financial transaction data (customer transactions, amounts, times, etc.) are collected and preprocessed. Data may include labels for normal and fraudulent transactions (e.g., 1=fraudulent, 0=normal).
2. Feature engineering: extract useful features from the data and convert them to the appropriate format. For example, it may extract normalization of amounts, encoding of time features, aggregate statistics of transactions, etc.
3. Model Selection: select an explanatory machine learning model. Models such as random forests and decision trees are suitable for assessing the importance of features and improving the interpretability of fraud detection.
4. Train the model: Train the selected model using the dataset. Training may involve splitting the training set and the test set, cross-validating and adjusting parameters.
5. Interpreting the model: Interpret the trained model and evaluate the reasons for fraud detection and the importance of the features. In the case of random forests, feature importance can be obtained and evaluated.
6. Fraud detection: Based on the interpretation results, fraud detection is performed using new transaction data as input. Provide transparency to financial institutions and supervisory authorities by explaining which features and rules the model has based its fraud detection on.

The following is a simple example implementation using random forests with scikit-learn.

from sklearn.ensemble import RandomForestClassifier

# Data Preparation and Preprocessing
X = ...  # Feature Vector of Transaction Data
y = ...  # Class label for fraudulent transactions (e.g., 1=fraudulent, 0=normal)

# Model Selection and Learning
model = RandomForestClassifier()
model.fit(X, y)

# Assessing the importance of features
importances = model.feature_importances_

# Tamper Detection
transaction_data = ...  # New transaction data
fraud_detection = model.predict(transaction_data)

# Indication of feature importance
for i, importance in enumerate(importances):
    print(f"Feature {i+1}: Importance={importance}")

# Display of fraud detection results
print("Fraud Detection:", fraud_detection)

In this example, a random forest is used to perform fraud detection for financial transactions and display the importance of the features. The model is trained on a dataset and then uses new transaction data as input for fraud detection.

Example of python implementation of image diagnosis in medical diagnostics using explainable machine learning

As an example of an implementation of image diagnosis in medical diagnostics using explainable machine learning, the following is a general procedure in Python.

1. Data preparation: medical image data (e.g., X-ray, MRI, CT scan) is collected and preprocessed. Data may include image files and associated correct labels (e.g., presence of disease).
2. Feature Engineering: Perform preprocessing to extract useful features from the image data. For example, it may involve image resizing, grayscaling, image boundary detection, extraction of specific regions, etc.
3. Model Selection: select an explanatory machine learning model. Models such as convolutional neural networks (CNN) described in “Overview of CNN and examples of algorithms and implementations” are well suited for learning features of image data and improving interpretability.
4. Train the model: train the selected model using a data set. Training may involve splitting the data into training and test sets, data expansion, and parameter adjustment.
5. Model Interpretation: Interpret the learned model and evaluate the reasons for the diagnostic results and the importance of the features. For example, a method such as Grad-CAM (Gradient-weighted Class Activation Mapping) can be used to visualize the regions of interest in the model.
6. Image Diagnosis: Generate diagnostic results based on the interpretation results, using new image data as input. Provide transparency to healthcare professionals and patients by explaining which features and regions the model based its diagnostic results on.

The following is a simple example of a CNN implementation using TensorFlow and Keras.

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

# Data Preparation and Preprocessing
X = ...  # image data
y = ...  # Disease class label (e.g., 1=disease present, 0=no disease)

# Building the Model
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation="relu", input_shape=(image_height, image_width, channels)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, kernel_size=(3, 3), activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation="relu"))
model.add(Dense(num_classes, activation="sigmoid"))

# Model Learning
model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
model.fit(X, y, batch_size=batch_size, epochs=num_epochs, validation_split=validation_split)

# Interpreting the Model
# Use methods such as Grad-CAM to visualize the areas of interest to the model

# image diagnosis
image_data = ...  # New image data
diagnosis = model.predict(image_data)

# Display of diagnostic results
print("Diagnosis:", diagnosis)

In this example, a CNN is used to perform image diagnosis and display diagnostic results. The model is trained on the dataset and then uses new image data as input to generate the diagnostic results.

Example of python implementation of disease prediction in medical diagnosis using explainable machine learning

As an example of an implementation of disease prediction in medical diagnosis using explainable machine learning, the following is a general procedure in Python.

1. Data preparation: medical data (patient characteristics, test results, symptoms, etc.) are collected and preprocessed. Data may include disease status and other relevant information.
2. Feature engineering: extract useful features from the data and convert them to an appropriate format. For example, categorical variables may be One-Hot encoded or numerical variables normalized.
3. Model Selection: select an explanatory machine learning model. Models such as logistic regression and decision trees are suitable for assessing the contribution of features and improving the interpretability of disease predictions.
4. Train the model: train the selected model using the dataset. Training may involve splitting the model into training and test sets, cross-validating, and adjusting parameters.
5. Interpreting the model: Interpret the trained model and evaluate the reasons for disease prediction and the importance of the features. For example, the coefficients of features and the rules of the decision tree can be checked to understand how much a particular feature contributes to the prediction.
6. Disease prediction: Based on the interpretation results, disease prediction is performed using new patient data as input. Provide transparency to healthcare providers and patients by explaining which features and rules the model has based its predictions on.

The following is a simple example implementation using logistic regression with scikit-learn.

from sklearn.linear_model import LogisticRegression

# Data Preparation and Preprocessing
X = ...  # Patient Characteristics Vector
y = ...  # Disease class label (e.g., 1 = patient present, 0 = no patient present)

# Model Selection and Learning
model = LogisticRegression()
model.fit(X, y)

# Assessing the importance of features
importances = model.coef_[0]

# Disease Prediction
patient_data = ...  # New patient data
disease_prediction = model.predict(patient_data)

# Indication of feature importance
for i, importance in enumerate(importances):
    print(f"Feature {i+1}: Importance={importance}")

# Display of disease prediction results
print("Disease Prediction:", disease_prediction)

In this example, logistic regression is used to make disease predictions and display the importance of features. The model is trained on the dataset and then uses new patient data as input for disease prediction.

Example of python implementation of decision making in automated vehicle operation

As an example of an implementation of automated vehicle decision making using explainable machine learning, the following is a general procedure in Python.

1. Data Preparation: Sensor data (e.g., camera, radar, LiDAR) and control inputs (e.g., steering angle, gas pedal, brakes) for the vehicle are collected and preprocessed. Data may include driving conditions, obstacle locations, speeds, vehicle control parameters, etc.
2. Feature engineering: extracting useful features from the data and converting them to an appropriate format. For example, sensor data may be processed to extract obstacle distances, speeds, etc., or to normalize control inputs.
3. Model Selection: select an explanatory machine learning model. Models such as decision trees and random forests are good for visualizing the decision-making process and improving explanatory power.
4. Train the model: Train the selected model using a data set. Training may involve splitting the model into training and test sets, cross-validating, and adjusting parameters.
5. Interpreting the model: Interpret the trained model to evaluate the reasons for the decision and the importance of the features. In the case of decision trees, it is possible to visualize the tree structure and obtain feature importance.
6. Automated decision making: Based on the interpretation results, make automated decisions. Explain which features and rules the model made decisions based on to ensure safety and transparency.

The following is a simple implementation example using decision trees with scikit-learn.

from sklearn.tree import DecisionTreeClassifier

# Data Preparation and Preprocessing
X = ...  # Feature vector of sensor data and control inputs
y = ...  # Class labels for decisions (e.g., go straight, turn right, turn left)

# Model Selection and Learning
model = DecisionTreeClassifier()
model.fit(X, y)

# Interpretation of decisions
# Tree Visualization
from sklearn.tree import export_graphviz
export_graphviz(model, out_file="decision_tree.dot", feature_names=["feature1", "feature2", ...])

# Indication of feature importance
importances = model.feature_importances_
for i, importance in enumerate(importances):
    print(f"Feature {i+1}: Importance={importance}")

# Automated Decision Making
sensor_data = ...  # Sensor data and control input data
decision = model.predict(sensor_data)

# Display of decision-making results
print("Decision:", decision)

In this example, decision trees are used to make automated decisions, visualizing the tree and displaying the importance of features. The model is trained on a dataset and then makes decisions using sensor data and control input data as input.

Example of python implementation of collision prediction in automated vehicle operation

As an example of an implementation of crash prediction in automated vehicle operation using explainable machine learning, the following is a general procedure in Python.

1. Data preparation: collect and preprocess crash-relevant data, such as vehicle sensor data (e.g., cameras, radar, LiDAR), information from surrounding vehicles, and vehicle control inputs.
2. Feature engineering: extracting useful features from the data and converting them to an appropriate format. For example, the distance and speed of obstacles may be extracted from sensor data, or the location and speed of surrounding vehicles may be used as features.
3. Model Selection: Select an explanatory machine learning model. Models such as random forests and decision trees are suitable for assessing the importance of features and improving the interpretability of crash prediction.
4. Train the model: Train the selected model using the dataset. Training may involve splitting the training set and test set, cross-validating and adjusting parameters.
5. Interpreting the model: Interpret the trained model and evaluate the reasons for predicting collisions and the importance of features. In the case of random forests, feature importance can be obtained and evaluated.
6. Crash Prediction: Based on the results of the interpretation, predict the crash of the car. Explain which features or rules the model was based on to make the crash prediction, to ensure safety and transparency.

The following is a simple implementation example using random forests with scikit-learn.

from sklearn.ensemble import RandomForestClassifier

# Data Preparation and Preprocessing
X = ...  # Sensor data, information on surrounding vehicles, and feature vectors of control inputs
y = ...  # Collision class (e.g., colliding, non-colliding)

# Model Selection and Learning
model = RandomForestClassifier()
model.fit(X, y)

# Assessing the importance of features
importances = model.feature_importances_

# Collision prediction
sensor_data = ...  # Sensor data, information on surrounding vehicles, and control input data
collision_prediction = model.predict(sensor_data)

# Indication of feature importance
for i, importance in enumerate(importances):
    print(f"Feature {i+1}: Importance={importance}")

# Display of collision prediction results
print("Collision Prediction:", collision_prediction)

In this example, a random forest is used to predict vehicle crashes and display the importance of features. The model is trained on a dataset and then uses sensor data, information from surrounding vehicles, and data from control inputs as input for crash prediction.

Example implementation in python of online ad recommendation

As an example of an implementation of online ad recommendation using machine learning that can be explained, the following is a general procedure in Python.

1. Data Preparation: Collect and preprocess data about online ads. Data may include ad characteristics (number of clicks, number of times shown, ad attributes, etc.) and user characteristics (age, gender, geography, etc.).
2. Feature engineering: extracting useful features from the data and converting them to an appropriate format. For example, categorical features may be One-Hot encoded or numeric features may be normalized.
3. Model Selection: select an explanatory machine learning model. Models such as random forests and gradient boosting are well suited to improve the explanatory nature of recommendations because of their ability to evaluate the importance of features.
4. Train the model: train the selected model using the dataset. Training may involve splitting the dataset into a training set and a test set, cross-validating and adjusting parameters.
5. Model interpretation: interpret the trained model and evaluate the importance of the features. For random forests, the feature_importances_ attribute may be used to obtain the importance of each feature.
6. Generate Recommendations: Based on the interpretation results, generate ad recommendations for the user. Transparency is provided to the user by adding information explaining the features used by the model to make predictions and their importance.

The following is a simple example implementation using random forests with scikit-learn.

from sklearn.ensemble import RandomForestClassifier

# Data Preparation and Preprocessing
X = ...  # feature vector
y = ...  # Click/non-click objective variable

# Model Selection and Learning
model = RandomForestClassifier()
model.fit(X, y)

# Assessing the importance of features
importances = model.feature_importances_

# Recommendation generation
user_data = ...  # User characteristics data
recommendation = model.predict(user_data)

# Indication of feature importance
for i, importance in enumerate(importances):
    print(f"Feature {i+1}: Importance={importance}")

# Display Recommendations
print("Recommendation:", recommendation)

In this example, a random forest is used to make ad recommendations and display feature importance. The model is trained on the dataset and then the recommendations are generated using the user’s feature data as input.

Reference Information and Reference Books

For more information on explainable machine learning techniques, see “Explainable Machine Learning.

Reference Book is “Hands-On Explainable AI (XAI) with Python”

“Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more”

“Explainable Machine Learning for Multimedia Based Healthcare Applications”

“Explainable and Interpretable Models in Computer Vision and Machine Learning”

“Interpretable Machine Learning with Python: Build explainable, fair, and robust high-performance models with hands-on, real-world examples”