Think Different

  Feature scaling is the process of normalizing the range of features in a dataset. This is done to ensure that all features are on a similar scale and that no single feature dominates the model. There are two main reasons why feature scaling is done in machine learning: To improve the performance of machine learning algorithms. To make the interpretation of machine learning models easier. Improving the performance of machine learning algorithms Machine learning algorithms often work better when the features are on a similar scale. This is because some machine learning algorithms are sensitive to the scale of the features. For example, a linear regression model will be more accurate if the features are all on a similar scale. Making the interpretation of machine learning models easier Feature scaling can also make the interpretation of machine learning models easier. This is because it can help to identify the most important features in the model. For example, if a feature is ...

Principal component analysis (PCA) is a statistical procedure that is used to reduce the dimensionality of data. It does this by finding a set of new variables that are uncorrelated with each other and that capture the most variance in the original data. For example, let's say we have a dataset of images of faces. Each image is a 100x100 pixel image, so it has 10,000 features (the pixel values). PCA can be used to reduce the dimensionality of this data by finding a set of 10 new variables that capture the most variance in the original data. These 10 new variables are called principal components. The first principal component will capture the most variance in the data, the second principal component will capture the second most variance, and so on. The principal components are ordered in decreasing order of variance. In the case of face images, the first principal component might capture the overall brightness of the image, the second principal component might capture the orientati...