Debugging Machine Learning Models with Python: Develop high-performance, low-bias, and explainable machine learning and deep learning models

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Publisher ‏ : ‎ Packt Publishing (September 15, 2023)
Language ‏ : ‎ English
Paperback ‏ : ‎ 344 pages
ISBN-10 ‏ : ‎ 1800208588
ISBN-13 ‏ : ‎ 978-1800208582
Item Weight ‏ : ‎ 1.32 pounds
Dimensions ‏ : ‎ 9.25 x 7.52 x 0.72 inches

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Debugging Machine Learning Models with Python: Develop high-performance, low-bias, and explainable machine learning and deep learning models

Machine learning models can sometimes be tricky to debug, especially when they are not performing as expected. In this post, we will explore some common debugging techniques using Python to develop high-performance, low-bias, and explainable machine learning and deep learning models.

1. Data preprocessing: One of the most common reasons for a model not performing well is improper data preprocessing. Make sure to handle missing values, scale or normalize features, encode categorical variables, and split the data into training and testing sets correctly.

2. Model selection: It’s essential to choose the right model architecture for your problem. Experiment with different algorithms, hyperparameters, and architectures to find the best model for your dataset.

3. Overfitting and underfitting: Keep an eye out for overfitting (high variance) or underfitting (high bias) in your model. Use techniques like cross-validation, regularization, and early stopping to prevent these issues.

4. Hyperparameter tuning: Fine-tuning hyperparameters can significantly impact the performance of your model. Use tools like GridSearchCV or RandomizedSearchCV to search for the best hyperparameters efficiently.

5. Visualization: Visualizing your data, model architecture, training/validation curves, and feature importance can help you understand your model better and identify potential issues.

6. Interpretability: Explainable AI is crucial for understanding how a model makes predictions. Use techniques like SHAP values, LIME, or feature importance to interpret and explain your model’s decisions.

By following these debugging techniques and best practices, you can develop high-performance, low-bias, and explainable machine learning and deep learning models with Python. Happy debugging!
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