How to select features using chi-squared in Python The Chi-Squared test is a statistical test that can be used to select features for a machine learning model. It tests the independence of two categorical variables by comparing the observed frequencies of the variables to the expected frequencies if they were independent. In Python, the Chi-Squared …
Day: January 30, 2019
How to select features using best ANOVA F-values in Python ANOVA F-values are a statistical measure that can be used to select features for a machine learning model. The F-value represents the ratio of the variance between two groups of data (in this case, the variance between the classes of your target variable) to the …
How to extract features using PCA in Python Principal Component Analysis (PCA) is a technique used to reduce the dimensionality of a dataset. It does this by finding the directions in which the data varies the most, and representing the data in terms of these directions. By representing the data in this way, it can …
How to reduce dimensionality using PCA in Python Principal Component Analysis (PCA) is a technique for dimensionality reduction that is commonly used in machine learning and data analysis. It works by identifying the directions (principal components) in the data that have the most variation and projecting the data onto these directions. By doing so, it …
How to reduce dimensionality on Sparse Matrix in Python One way to reduce the dimensionality of a sparse matrix in Python is by using the Singular Value Decomposition (SVD) technique. SVD is a matrix factorization method that can be used to decompose a matrix into three separate matrices: a matrix of singular values, a left …
How to determine Spearman’s correlation in Python Spearman’s correlation, also known as rank correlation, is a statistical method that is used to measure the strength of a monotonic relationship between two variables. It ranges from -1 to 1, where -1 indicates a strong negative correlation, 0 indicates no correlation, and 1 indicates a strong positive …