How to do recursive features elimination in Python using DecisionTreeRegressor Recursive feature elimination (RFE) is a technique used in machine learning to determine the most important features in a dataset. This is done by iteratively removing the least important feature until a certain number of features is reached. In Python, one can use the library …
How to do recursive features elimination in Python Recursive feature elimination (RFE) is a technique used in machine learning to determine the most important features in a dataset. It can be used to improve the accuracy and efficiency of a model by removing unnecessary features that do not contribute to the prediction. In Python, there …
How to drop out highly correlated features in Python In machine learning, correlated features can cause problems because they can provide redundant information to the model. Having too many correlated features can also increase the risk of overfitting. One way to deal with correlated features is to drop some of them. This process is called …
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 …
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 …
IRIS Flower Classification using Logistic Regression Classifier IRIS flower classification is a common problem in machine learning. The IRIS dataset is a well-known dataset that contains information about different types of IRIS flowers, including their sepal length, sepal width, petal length, and petal width. The goal of IRIS flower classification is to use this …