In this video, we will learn what is the curse of dimensionality which is faced by the Machine Learning practitioners while training their models.

While training a machine learning model more the input features we feed to the model better is the performance of the model. But this statement is true upto a certain extent after which we the accuracy of the predictions start decreasing.

This problem arises because if we increase just the number of features our model becomes unable to generalize well on the data and as a result performance degrades.

To solve this problem we use dimensionality reduction techniques like PCA, LDA, t-SNE.