Do your models keep overfitting?

Overfitting happens when a model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new data.

Here are 6 key strategies to address it:

-Data Augmentation: Increasing the size and diversity of the training dataset can help the model generalize better.

-Cross-Validation: If you suspect your machine learning algorithm is overfitting. You'll use this to detect it. This helps in assessing how the model will generalize to an independent dataset.

-Simplifying the Model: Reducing the complexity of the model by using fewer layers or neurons in neural networks, or fewer features in other types of models, can prevent overfitting.

-Regularization: Techniques like L1 or L2 regularization add a penalty to the loss function based on the complexity of the model.

-Early Stopping: This involves monitoring the model's performance on a validation set during training and stopping the training process once the performance on the validation set starts to degrade.

-Feature Selection: Selecting a subset of relevant features can reduce overfitting, as irrelevant or partially relevant features can lead to a decrease in the performance of the model.

What are your methods to address overfitting while also avoiding underfitting?

Видео Do your models keep overfitting? канала Nikansh