Two methods are used to evaluate models:

1. Hold-out
2. Cross-validation

We use a test data set to evaluate the performance of the model. This test data set should not be part of the training of the model. Otherwise, the model will suffer from overfitting. In Hold-out method, the given data set is divided randomly into three sets:

1. Training set
2. Validation set
3. Test set.

When the data available is limited, we use the Cross-validation method. Here, the data set is divided into  'k' number of equal subsets. We build a model for each set. It is also known as K-fold Cross-validation. The categories of models under supervised learning are:

1. Regression
2. Classification.

The  corresponding  methods for evaluation of these models are also categorized as:

1. Evaluation of Regression Models
2. Evaluation of Classification Models.

In the evaluation of regression models, we are concerned with the continuous values whereas, in the evaluation of classification models, we try to find out the error between the actual value and the predicted value. Here in the classification models, our concern is on the correct and incorrect classification of the number of data points. We try to find out the confusion matrix and calculate the ROC curve to help us better in model evaluation.

Confusion matrix:

From the confusion matrix we find out the FOLLOWING:

• True Positive, True Negative, FALSE positive and False-negative.
• ROC Curve
• ROC Curve stands for Receiver Operating Characteristics Curve.For evaluating a model using a ROC curve, we MEASURE the area under the.

ROC curve:

It is the RATIO of True Positive Rate (TPR) to the False Positive Rate (FPR).

There are some other evaluation methods also for the evaluation of classification models such as:

1. GAIN and Lift charts
2. Gini coefficient etc.

The often-used methods are the confusion matrix and the ROC curve.