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Does Gaussian naive Bayes require feature scaling?
Naive Bayes Algorithm Feature Scaling Why Naive Bayes Algorithm is NOT affected by Feature Scaling is a short video regarding, Why Naive Bayes Machine Learning algorithm is NOT affected by feature magnitude, hence Naive Bayes does not need feature scaling.
Is naive Bayes affected by scaling?
Note: Naive Bayes, Linear Discriminant Analysis, and Tree-Based models are not affected by feature scaling. In Short, any Algorithm which is Not Distance based is Not affected by Feature Scaling.
When to use Gaussian naive Bayes in ML?
Gaussian Naive Bayes has high functionality. Use when the dimension of input is high. The dataset is of two parts: FEATURE MATRIX and RESPONSE VECTOR. The feature matrix contains all vectors of the dataset which consists of dependent features.
When to use Gaussian Bayes in machine learning?
Gaussian is the easiest to find out the mean and the standard deviation from the training data. It follows Gaussian normal distribution and supports continuous data. Thus supervised machine learning classification algorithm based on Bayes theorem. Gaussian Naive Bayes has high functionality. Use when the dimension of input is high.
How is the feature importance determined in naive Bayes?
Naïve Bayes methods work by determining the conditional and unconditional probabilities associated with the features and predict the class with the highest probability. Thus, there are no coefficients computed or associated with the features you used to train the model (compare with its documentation ).
How is the ML algorithm sensitive to scaling?
The ML algorithm is sensitive to the “relative scales of features,” which usually happens when it uses the numeric values of the features rather than say their rank. In many algorithms, when we desire faster convergence , scaling is a MUST like in Neural Network.