Why is decision tree better than logistic regression?

Why is decision tree better than logistic regression?

Decision Trees bisect the space into smaller and smaller regions, whereas Logistic Regression fits a single line to divide the space exactly into two. Of course for higher-dimensional data, these lines would generalize to planes and hyperplanes.

Why would you use a decision tree instead of a regression method?

When there are large number of features with less data-sets(with low noise), linear regressions may outperform Decision trees/random forests. In general cases, Decision trees will be having better average accuracy. For categorical independent variables, decision trees are better than linear regression.

What is the biggest weakness of decision trees compared to logistic regression Clas Sifiers?

What is the biggest weakness of decision trees compared to logistic regression classifiers? Decision trees are more likely to overfit the data since they can split on many different combination of features whereas in logistic regression we associate only one parameter with each feature.

What is better than logistic regression?

Tree-Based Methods Classification And Regression Tree (CART) is perhaps the best well known in the statistics community. For identifying risk factors, tree-based methods such as CART and conditional inference tree analysis may outperform logistic regression.

Why logistic regression is the best?

Logistic regression is a simple and more efficient method for binary and linear classification problems. It is a classification model, which is very easy to realize and achieves very good performance with linearly separable classes. It is an extensively employed algorithm for classification in industry.

When should I use logistic regression?

Logistic regression is applied to predict the categorical dependent variable. In other words, it’s used when the prediction is categorical, for example, yes or no, true or false, 0 or 1. The predicted probability or output of logistic regression can be either one of them, and there’s no middle ground.

What’s the difference between a decision tree and logistic regression?

You’ll want to keep in mind though that a logistic regression model is searching for a single linear decision boundary in your feature space, whereas a decision tree is essentially partitioning your feature space into half-spaces using axis-aligned linear decision boundaries.

When to use a decision tree or categorical data?

When you are sure that your data set divides into two separable parts, then use a Logistic Regression. If you’re not sure, then go with a Decision Tree. A Decision Tree will take care of both. Categorical data works well with Decision Trees, while continuous data work well with Logistic Regression.

Is the result of logistic regression always linear?

Result of logistic regression for our sample data will be like this. You can see that, it doesn’t do a very good job. Because whatever you do, decision boundary produced by logistic regression will always be linear , which can not emulate a circular decision boundary which is required.

When does logistic regression outperform tree induction?

In technical terms, if the AUC of the best model is below 0.8, logistic very clearly outperformed tree induction. You have have low signal to noise for a number of reasons – the problem is just inherently unpredictable (think stock market) dataset or it is too small to ‘find the signal’.