What is the effect of adding more features to a linear regression model?

What is the effect of adding more features to a linear regression model?

Step 1: Understand what features are. Features are the input variables used in a model to make predictions.
Step 2: Know that a linear regression model tries to find a relationship between features and the target variable.
Step 3: Adding more features means including more input variables in the model.
Step 4: More features can help the model learn more complex patterns in the data.
Step 5: However, if the added features are not relevant, they can confuse the model.
Step 6: This confusion can lead to overfitting, where the model learns the noise in the training data instead of the actual pattern.
Step 7: Overfitting makes the model perform poorly on new, unseen data.
Step 8: Additionally, having too many features can make the model harder to understand and interpret.

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