Before Overfitting. In machine learning and especially in supervised machine learning. Every dataset has a feature vector and a label which is a class, every feature vector belongs to a particular class. we use this feature vector to generate training, validation and testing dataset. Sometimes needs to make the training dataset balance we use RandomOverSampler() such that the more will not dominate. collectively multiple feature vectors are called a feature matrix. We will feed the training feature vectors to our ML model and then will compare the prediction with the actual check for the losses and make the adjustments to the model for the validation. I drew three graphs that depicts the decision boundary line of our ML model between two classes (white & yellow). let's understand overfitting with these graphs down below:

NOOOOOO Context: She cheats on two guys at the same time

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Tried to make this SVG. Next will try some different gradient or color, and also make one f1 car. Let's see how it goes