Evaluate your Regression model like a Pro! Understand these Evaluation Metrics and make faster desicions on your models.

In order to understand how your Regression model performs we evaluate the model on both the training and test data. Evaluate here means using a metric to quantify the Regression model performance using just a single number.

The fit of the model can be understood well by comparing these evaluation metrics for both train and test data.

Consider initial data= Information + Noise
Here in the above case the resulting underfit model will have less of information and no noise.

Consider initial data= Information + Noise
Here in the above case the resulting overfit model will have everything information as well as noise.

Hence the aim is the find the model which best fits the data ie only the entire information with no noise.

2. First metric to look at for linear regression to see the model performance.

3. If R-squared is higher, its considered better.

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