Reason for Overfitting

Let’s discuss what are the reasons that cause overfitting to the machine learning model which are listed below,

1. Assigning a complex model that has too many parameters is more likely to overfit the training data.
2. when the training dataset is too small the model may not be able to learn the underlying patterns in the data and may start to learn the noise in the data as well.
3. when the training data is highly imbalanced with one output class so model may learn to bias its predictions toward the majority class.
4. In feature engineering, the features are not properly scaled or engineered thus the model leads to overfitting.
5. In feature selection, selected features for training the model are not relevant to the target variable, it is more likely to overfit the training data.
6. If the model trains the data too long, it may start to learn the noise in the data and it tends to overfitting.

The effectiveness of a machine learning model is measured by its ability to make accurate predictions and minimize prediction errors. An ideal or good machine learning model should be able to perform well with new input data, allowing us to make accurate predictions about future data that the model has not seen before. This ability to work well with future data (unseen data) is known as generalization. To consider how well a machine learning model learns and generalizes to new data, we are going to examine the concept of overfitting which is the key factor that can significantly impact the performance of machine learning algorithms on future data, and also going to discuss the Regularization concept which will try to avoid overfitting in machine learning.

In this article, we will cover the Overfitting and Regularization concepts to avoid overfitting in the model with detailed explanations.