I would like to train a DecisionTree using sklearn Pipeline. My goal is to predict the ‘language’ column, using the ‘tweet’ as ngram transformed features. However I am not able to make the LabelEncoder transformation works for the ‘language’ column inside a pipeline. I saw that there is a common error, but also if I try the suggested method to
Tag: decision-tree
How to retrieve the full branch path leading to each leaf node of a sklearn Decision Tree?
I have this decision tree, which I would like to extract every branch from it. The image is a portion of the tree, since the original tree is much bigger but it doesn’t fit well on a single image. I’m not trying to print the rules of the tree like or like: What I’m trying to achieve is something like:
Decision tree with a probability target
I’m currently working on a model to predict a probability of fatality once a person is infected with the Corona virus. I’m using a Dutch dataset with categorical variables: date of infection, fatality or cured, gender, age-group etc. It was suggested to use a decision tree, which I’ve already built. Since I’m new to decision trees I would like some
Plot Decision Tree train/test accuracy against max depth
I was trying to plot the accuracy of my train and test set from a decision tree model. Since I am new to using python, I wasn’t sure what type of graphing package I should use. I have used a simple for loop for getting the printed results, but not sure how ]I can plot it. Thanks! My code: Desired
Why does this decision tree’s values at each step not sum to the number of samples?
I’m reading about decision trees and bagging classifiers, and I’m trying to show the first decision tree that is used in the bagging classifier. I’m confused about the output. Here’s a snippet out of the output It’s been my understanding that the value is supposed to show how many of the samples are classified as each category. In that case,
What does the value of ‘leaf’ in the following xgboost model tree diagram means?
I am guessing that it is conditional probability given that the above (tree branch) condition exists. However, I am not clear on it. If you want to read more about the data used or how do we get this diagram then go to : http://machinelearningmastery.com/visualize-gradient-boosting-decision-trees-xgboost-python/ Answer Attribute leaf is the predicted value. In other words, if the evaluation of a