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Is decision tree sensitive to imbalanced dataset?
The decision tree algorithm is effective for balanced classification, although it does not perform well on imbalanced datasets. The split points of the tree are chosen to best separate examples into two groups with minimum mixing.
What are attributes and weights in decision tree?
Thus, a decision tree is first constructed using the input cases. For each attribute, which may appear in several places in the tree, the weight is determined by taking the sum of its information gain at each appearance multiplied by the percent of input cases classified by that attribute.
How does decision Trees handle imbalanced data?
Decision trees frequently perform well on imbalanced data. They work by learning a hierarchy of if/else questions and this can force both classes to be addressed. While our accuracy score is slightly lower, both F1 and recall have increased as compared to logistic regression!
What is Sample_weight in Sklearn?
sample_weight augments the probability estimates in the probability array which augments the impurity measure which augments how nodes are split which augments how the tree is built which augments how feature space is diced up for classification.
What does the ordinal feature in a decision tree mean?
As per my knowledge, it doesn’t matter for a decision tree model whether the features are ordinal or categorical. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. Decision trees describe patterns by using a list of attributes.
What is the goal of a decision tree?
The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. Decision trees describe patterns by using a list of attributes. For a more detailed explanation, I am providing here some links which you will find helpful for such queries.
Which is an example of an ordinal feature?
Let us consider the following example. You are predicting weights of humans. You have the mapping {‘S’: 0, ‘XL’: 1, ‘L’: 2, ‘M’: 3} for a feature of t-shirt size. The GBM does its splitting down to the last branch and has yet to split on the t-shirt size feature.
How are categorical and ordinal data treated in GBM?
Ordinal data and Categorical data are treated exactly the same in GBM. However, the results will most likely be different if you have ordinal data formatted as categorical. This would also be true of categorical data formatted as ordinal.