Model Evaluation Metrics and validation of the model for Machine Learning Everyone should know

Evaluating the Model is considered a core part of building an effective machine learning model. Evaluating the model allows us to ask important questions like

1. How well is my model doing? Is it a useful model?
2. Will training my model on more data improve its performance?
3. Do I need to include more features?

Evaluation metrics explain the performance of a model. An important aspect of evaluation metrics is their capability to discriminate among model results. Evaluating the Model helps in improving the performance of the model by checking the accuracy of your model before computing the predicted value.

They are different kinds of metrics to evaluate our models. The choice of metric completely depends on the type of model and the implementation of the model.

They are different metrics that can help in evaluating the model accuracy.

1. Confusion Matrix: Confusion Matrix is an n x n matrix, where n is the number of the class being predicted. For example, a decision tree has two class variables then we will have a Confusion Matrix of 2 x 2.

Confusion Matrix