Overfitting in machine learning

Overfitting occurs when our machine learning model tries to cover all the data points or more than the required data points present in the given dataset.

• How to prevent overfitting in machine learning

1. Cross-validation
   Use your initial training data to generate multiple mini train-test splits. Use these splits to tune your model.
2. Train with more data
   It won't work every time, but training with more data can help algorithms detect the signal better.
3. Remove features
   For those that don't have built-in feature selection, you can manually improve their generalizability by removing irrelevant input features.
4. Early stopping
   Up until a certain number of iterations, new iterations improve the model. After that point, however, the model's ability to generalize can weaken as it begins to overfit the training data.