Does feature scaling affect logistic regression?

Summary. We need to perform Feature Scaling when we are dealing with Gradient Descent Based algorithms (Linear and Logistic Regression, Neural Network) and Distance-based algorithms (KNN, K-means, SVM) as these are very sensitive to the range of the data points.

Which models are affected by feature scaling?

The Machine Learning algorithms that require the feature scaling are mostly KNN (K-Nearest Neighbours), Neural Networks, Linear Regression, and Logistic Regression.

Does scaling affect model performance?

All Answers (5) Feature scaling usually helps, but it is not guaranteed to improve performance. If you use distance-based methods like SVM, omitting scaling will basically result in models that are disproportionally influenced by the subset of features on a large scale.

Should I scale for logistic regression?

Standardization isn’t required for logistic regression. The main goal of standardizing features is to help convergence of the technique used for optimization. For example, if you use Newton-Raphson to maximize the likelihood, standardizing the features makes the convergence faster.

What are hyperparameters in logistic regression?

Machine learning algorithms have hyperparameters that allow you to tailor the behavior of the algorithm to your specific dataset. Hyperparameters are different from parameters, which are the internal coefficients or weights for a model found by the learning algorithm.

Is feature scaling necessary for multiple linear regression?

For example, to find the best parameter values of a linear regression model, there is a closed-form solution, called the Normal Equation. If your implementation makes use of that equation, there is no stepwise optimization process, so feature scaling is not necessary.

What is the advantage of feature scaling?

Another reason why feature scaling is applied is that gradient descent converges much faster with feature scaling than without it. It’s also important to apply feature scaling if regularization is used as part of the loss function (so that coefficients are penalized appropriately).

Why is feature scaling needed?

Feature scaling is essential for machine learning algorithms that calculate distances between data. Therefore, the range of all features should be normalized so that each feature contributes approximately proportionately to the final distance.

How can you increase the accuracy of a logistic regression model?

Hyperparameter Tuning – Grid Search – You can improve your accuracy by performing a Grid Search to tune the hyperparameters of your model. For example in case of LogisticRegression , the parameter C is a hyperparameter. Also, you should avoid using the test data during grid search. Instead perform cross validation.

What are model hyperparameters?

A model hyperparameter is a configuration that is external to the model and whose value cannot be estimated from data. They are often used in processes to help estimate model parameters. They are often specified by the practitioner. They can often be set using heuristics.

Does scaling of features affect the result of logistic regression?

I was under the belief that scaling of features should not affect the result of logistic regression. However, in the example below, when I scale the second feature by uncommenting the commented line, the AUC changes substantially (from 0.970 to 0.520): I believe this has to do with regularization (which is a topic I haven’t studied in detail).

When do we need to perform feature scaling?

We need to perform Feature Scaling when we are dealing with Gradient Descent Based algorithms (Linear and Logistic Regression, Neural Network) and Distance-based algorithms (KNN, K-means, SVM) as these are very sensitive to the range of the data points. This step is not mandatory when dealing with Tree-based algorithms.

Can regregression be used for classification problems?

Regression can also be used for classification problems. The first natural example of this is logistic regression. In binary classifation (two labels), we can think of the labels as 0 & 1. Once again denoting the predictor variable as x, the logistic regression model is given by the logistic function F ( x) = 1 1 + e − ( a x + b).

Is there a penalty on coefficent size in logistic regression?

The implementation of logistic regression you use has a penalty on coefficent size (L1 or L2 norm). In t Your do not train your model until the full convergence of the gradient descent. As a result, the speed of update of each feature depends on its scale, and when you finish, some coefficients are closer to the optimum than the others.