In simple terms, think of variance as the error rate of the testing data. Variance is the error rate of the y_predicted and y_test Again, the predicted values are y_predicted. Let’s assume we have trained the model and this time we are trying to predict values with input ‘x_test’. When the error rate is high, we call it High Bias and when the error rate is low, we call it Low Bias Variance In simple terms,think of bias as the error rate of the training data. Bias is the error rate of y_predicted and y_train. Let’s assume we have trained the model and are trying to predict values with input ‘x_train’. The error rate is the average error of value predicted by the model and the correct value. Y_predicted // the values predicted by the model given an input X_test, y_test // This is the testing data Overfitting and Underfitting in ClassificationĪssume you have a classification model, training data and testing data x_train, y_train // This is the training data.Overfitting and Underfitting in Regression.
