I'll also show you two different ways to visualize the Confusion Matrix.
You'll also gain practical skills to generate and visualize these metrics using Scikit-Learn and Seaborn.
I'll show you two ways using Python and Scikit-Learn's helper functions - cross_val_score() and cross_validate().
Cross-Validation builds upon Train Test Split and provides a better estimate of a machine learning model's performance.
Here's how you can do it using pandas, Matplotlib, and Seaborn.
Here's why you should use robust scaling to handle outliers.
Here's how you can generate custom statistics using the agg() method.
We must scale such features. Here’s how sklearn's Standard and MinMax scalers can help.
Let's explore how you can stop overfitting dead in it's tracks and train your models with confidence.
We'll explore a few ways and discuss why using ColumnTransformer is the best approach.