50+ Common scikit-learn Mistakes and Solutions for Machine Learning Problems

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In this post, I cover various common mistakes and their solutions in using scikit-learn (sklearn), with a focus on details that beginners might overlook. The mistakes span different stages of the machine learning workflow, including data preprocessing, model training, evaluation, and handling specific scenarios. Here is an overview:

Data Preprocessing Mistakes:

• Not addressing missing values properly.
• Not encoding categorical variables.
• Scaling features before train-test split.
• Normalizing data without handling outliers.
• Not converting text data into numerical features.
• Dropping rows with missing values.

Model Training and Evaluation Mistakes:

• Using the wrong model for classification.
• Not performing feature selection.
• Not addressing imbalanced classes.
• Scaling features before train-test split.
• Not using cross-validation for model evaluation.
• Using the wrong metric for evaluation in a regression problem.