Ever have a question that, “Why is Linear Regression giving me such good accuracy on the training set but a low accuracy on the test set in spite of adding all the available dependent features to the model?” The question above seems inexplicable to many people but is answered by a concept called...
You might wonder why do we need cross validation in the first place itself. Let’s explain that first. Normally, the generalization performance of a machine learning algorithm depends on its prediction capability on an independent test data. This assessment is of utmost importance to us. Cross Validation is such a model validation technique...
A machine learning model consists of various parameters that need to be learned from the data. The crux of Machine learning is fitting a model to the data. This process of training a model with existing data to fit the model parameters, is called model training. Hyperparameters refer to another kind of parameters...
Overfitting happens mostly because the model becomes too complex. Such a model will give poor accuracies, as it memorizes the noise in the training data. A model is usually fit by achieving the highest accuracy on the training data set. However, its efficiency is judged by its its performance on test data. Overfitting occurs...
