K-Nearest Neighbors (KNN) imputation is another method to handle missing data. It uses the ‘k’ closest instances (rows) to each instance that contains any missing values to fill in those values. In sklearn, you can… K-Nearest Neighbors (KNN) imputation in sklearn
Handling missing data is a common preprocessing task in machine learning. In scikit-learn, you can handle missing data by using imputation techniques provided by the SimpleImputer class or by employing other strategies like dropping rows/columns with missing… A comic guide to mean/median/mode imputation & Python codes
Singular Value Decomposition (SVD) is a powerful matrix decomposition technique that generalizes the concept of eigenvalue decomposition to non-square matrices. Eigenvalue decomposition specifically decomposes a square matrix into its constituent eigenvalues and eigenvectors. This decomposition… SVD for dimension reduction
To test for outliers in multivariate data in Python, you can use several libraries like numpy, scipy, pandas, sklearn, etc. Here’s how you can do it: Mahalanobis distance using Scipy library The Mahalanobis distance is a statistical measure used… test for outliers in multivariate data in Python
Example 1: Spam Detection Let’s say historically, 20% of emails are spam, so and the probability that the email is not spam is . Suppose the probability of observing the word “free” in a spam… Application of Bayesian theorem in spam detection & medical diagnosis