#13 PCA
PCA (Principal Component Analysis) is a dimensionality reduction technique and helps us to reduce the number of features in a dataset while keeping the most important information. It changes complex d

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Articles tagged with #machine-learning
PCA (Principal Component Analysis) is a dimensionality reduction technique and helps us to reduce the number of features in a dataset while keeping the most important information. It changes complex d

Linear Regression is a supervised machine learning algorithm used for predicting continuous numerical values. It finds the relationship between input variables and output variables using a straight li

Naive Bayes is a supervised machine learning algorithm mainly used for classification problems. It is based on Bayes’ Theorem and assumes that all features are independent. Naive Bayes is commonly us

K-Nearest Neighbors (KNN) is a simple supervised machine learning algorithm used for both classification and regression problems. It predicts the output based on the nearest data points. KNN helps th

C4.5 is an improved version of the ID3 Decision Tree algorithm. It was introduced by Ross Quinlan to overcome the limitations of ID3. This algorithm builds the decision tree using - "Entropy", "Inform

ID3 (Iterative Dichotomiser 3) is a Decision Tree algorithm used for classification problems. It was introduced by Ross Quinlan in 1986. The algorithm builds a Decision Tree by selecting the feature w
