How can I improve my classification models?

How can I improve my classification models?

You can improve your classification models by optimizing hyperparameters, using better features, and employing ensemble methods.

What are some techniques for feature selection?

Techniques for feature selection include recursive feature elimination, LASSO, and using tree-based methods to assess feature importance.

How do I handle imbalanced datasets?

You can handle imbalanced datasets by using techniques like resampling, synthetic data generation (SMOTE), or adjusting class weights.

What role does cross-validation play in model evaluation?

Cross-validation helps in assessing the model's performance by splitting the data into training and testing sets multiple times.

Can you recommend any libraries for building classification models?

Sure! Popular libraries include Scikit-learn, TensorFlow, and PyTorch for building and training classification models.