Introduction To AI In Automotive – Thomas Imre Cyrille Buidin

Long Description for Figure 4.10.7 FIGURE 4.10.7 Decision tree for brake wear classification with reduced features (continued). from sklearn.feature_selection import SelectKBest, f_classif X = pd.DataFrame(X, columns=[‘Feature 1’, ‘Feature 2’,…,’Feature_n’]) # Use SelectKBest to select the top k features # Here, ‘f_classif’ is used as the score function, which is suitable for classification tasks.

k = 2 # You can change k to select a different number of features selector = SelectKBest(score_func=f_classif, k=k) # Fit the selector to the training data X_train_selected = selector.fit_transform(X_train, Y_train) # Apply the same selection to the test set X_test_selected = selector.transform(X_test) # Now, let’s train a decision tree on the reduced feature set tree_name = DecisionTreeClassifier(random_state=42) tree_name.fit(X_train_selected, Y_train) # Make predictions and evaluate the model train_predictions = tree_name.

predict(X_train_selected) test_predictions = tree_name.predict(X_test_selected) train_accuracy = accuracy_score(Y_ train, train_predictions) test_accuracy = accuracy_score(Y_ test, test_predictions) print(f”Training Accuracy with top {k} features: {train_accuracy:.4f}”) print(f”Test Accuracy with top {k} features: {test_accuracy:.4f}”) #Print the selected feature names selected_features = [X.columns[i] for i in selector. get_support(indices=True)] print(f”Selected features: {selected_features}”) Training Accuracy with top 4 features: 1.0000 Test Accuracy with top 4 features: 0.7800 Selected features: [‘Max_Temperature_C’, ‘Average_Deceleration_g’, ‘Mileage_Since_Last_Maintenance_km’, ‘Vibration_Level_Hz’] # Plot the decision tree plt.figure(figsize=(20, 10)) plot_tree(tree_name, feature_names=selected_features, class_names=class_names, filled=True, rounded=True) plt.title(“Decision Tree for Brake Wear Classification with Reduced Features”) # Save the plot to an image file with high resolution plt.savefig(“reduced_features_decision_tree.png”, format=”png”, bbox_inches=”tight”, dpi=800) # Increase dpi for better quality plt.show() Step 11.

Tuning Depth of Tree Using cross-validation to experiment with different tree depths allows the model performance’s evaluation for various maximum depths and the selection of the optimal depth that balances bias and variance. 1. max_depths: This is the range of tree depths that need to be tested, from one to 20 in this case. It can be adjusted as needed. 2. cross_val_score: Performs cross-validation on the model for each depth. The cv= 5 parameter specifies fivefold cross-validation. The scoring= “accuracy” parameter evaluates the model based on accuracy.

3. Collecting Scores: The mean accuracy of cross-validation scores for each depth is calculated and stored in the cv_scores list. 4. Optimal Depth: After testing all depths, the depth that yields the highest mean accuracy is considered the optimal depth. 5. Visualization: The plot shows the relationship between tree depth and cross- validation accuracy (Figure 4.11), helping visualize the trade-off between underfitting and overfitting.

FIGURE 4.11 Evaluation of tree depth tuning.

This book serves as a timely and practical guide to understanding how artificial intelligence (AI) can be harnessed in automotive engineering. It not only demystifies complex AI methods but also equips readers with the tools to creatively apply them to their own engineering challenges. Drawing from the authors’ extensive teaching experience, this book adopts a hands- on approach to applying AI tools – such as regression, classification, clustering, and deep learning (DL) – to real-world engineering challenges.

Through clear explanations and step-by-step algorithmic structures, readers are introduced to foundational AI concepts and guided through practical applications using real automotive data. Examples include predicting electric vehicle energy consumption under varying conditions, assessing battery degradation, analyzing brake system wear, and implementing intelligent control for thermal management (HVAC systems). This book demonstrates how AI can optimize performance, enhance safety, and drive innovation. Each chapter builds essential skills, including setting up machine learning (ML) environments, implementing regression and classification models, constructing decision trees, applying clustering techniques, and designing neural networks (NNs).

This guide is essential for postgraduate students interested in exploring how AI can be applied in engineering. OceanofPDF.com Introduction to Artificial Intelligence in Automotive Engineering Understanding Theory and Practical Applications Thomas Imre Cyrille Buidin and Florin Emil Mariasiu OceanofPDF.com Designed cover image: Shutterstock First edition published 2026 by CRC Press 2385 NW Executive Center Drive, Suite 320, Boca Raton FL 33431 and by CRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN CRC Press is an imprint of Taylor & Francis Group, LLC © 2026 Thomas Imre Cyrille Buidin and Florin Emil Mariasiu Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use.

The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S.

This is a short excerpt from the opening of “” by Unknown, quoted for review and introduction purposes. All rights belong to the copyright holders.

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