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Why are decision trees 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.
Which is better logistic regression or decision tree?
Decision trees simplify such relationships. A logistic regression can, with appropriate feature engineering, better account for such a relationship. A second limitation of a decision tree is that it is very expensive in terms of sample size.
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.
How are decision trees used in machine learning?
A decision tree is a supervised machine learning model used to predict a target by learning decision rules from features. As the name suggests, we can think of this model as breaking down our data by making a decision based on asking a series of questions.
Can a decision tree be affected by an outlier?
While a Decision Tree, at the initial stage, won’t be affected by an outlier, since an impure leaf will contain nine +ve and one –ve outlier. The label for the leaf will be +ve, since the majority are positive.