Sage-Tips

When would you use random Forest vs logistic regression for binary classification problem?

Logistic regression performs better when the number of noise variables is less than or equal to the number of explanatory variables and the random forest has a higher true and false positive rate as the number of explanatory variables increases in a dataset.

When should I use random forest regression?

Why use Random Forest Algorithm Random forest algorithm can be used for both classifications and regression task. It provides higher accuracy through cross validation. Random forest classifier will handle the missing values and maintain the accuracy of a large proportion of data.

What is one reason for using a random forest instead of a linear regression?

The averaging makes a Random Forest better than a single Decision Tree hence improves its accuracy and reduces overfitting. A prediction from the Random Forest Regressor is an average of the predictions produced by the trees in the forest.

Why is logistic regression worse than random forest?

variables exceeds the number of explanatory variables, random forest begins to have a higher true positive rate than logistic regression. As the amount of noise in the data increases, the false positive rate for both models also increase.

Can random forest be used for logistic regression?

We presented a large-scale benchmark experiment for comparing the performance of logistic regression and random forest in binary classification settings. The overall results on our collection of 243 datasets showed better accuracy for random forest than for logistic regression for 69.0\% of the datasets.

Can I use random forest for binary classification?

Random forests is a supervised learning algorithm. It can be used both for classification and regression. It is also the most flexible and easy to use algorithm.

Can I use random forest for regression?

In addition to classification, Random Forests can also be used for regression tasks. A Random Forest’s nonlinear nature can give it a leg up over linear algorithms, making it a great option.

Can random forest be used for linear regression?

Is Random Forest logistic regression?

Logistic regression is used to measure the statistical significance of each independent variable with respect to probability. Random forest works on decision trees which are used to classify new object from input vector.

Can we apply linear regression instead of logistic regression and vice versa?

In a classification problem, the target variable(or output), y, can take only discrete values for a given set of features(or inputs), X. Linear Regression is a supervised regression model. Logistic Regression is a supervised classification model. In Linear Regression, we predict the value by an integer number.

How do you use random forest classification?

It works in four steps:

1. Select random samples from a given dataset.
2. Construct a decision tree for each sample and get a prediction result from each decision tree.
3. Perform a vote for each predicted result.
4. Select the prediction result with the most votes as the final prediction.

Should I use random forest or logistic regression for my data?

Do not forget to tune the parameters of logistic regression / random forest for maximizing their performance on your data. If your data is categorical, then random forest should be your first choice; however, logistic regression can be dealt with categorical data [ 1].

Can random forest algorithm be used for feature selection?

Random Forests can be used for feature selection because if you fit the algorithm with features that are not useful, the algorithm simply won’t use them to split on the data. It’s possible to extract the ‘best’ features (which could be the total number of times a feature was used to split on the data, or the mean decrease in impurity etc).

What can we learn from logistic regression?

The logistic regression gives us the one thing the random forest could never provide: an explanation for people like management of corporations and governments who can then turn around and try to implement solutions. Ask for help: at Lambda we have a 20 minute rule where we ask for help if we still can’t figure it out on our own.

Should I use a decision tree or a logistic regression?

If your data is linearly separable, go with logistic regression. However, in real world, data is rarely linearly separable. Most of the time data would be a jumbled mess. In such scenarioes, Decision trees would be a better fit as DT essentially is a non-linear classifier.