How does extratrees classifier work in ML in python
This recipe explains how does extratrees classifier work in ML in python
Recipe Objective
Extratree classsifer used multiple tree for building classification model. For feature selection, one of the most powerful tool is extra tree classifier. It works best in presence of noisy features.
So this recipe is a short example on how does extratree classifer works. Let's get started.
Step 1 - Import the library
import pandas as pd
import numpy as np
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.datasets import load_iris
Let's pause and look at these imports. Numpy and Pandas are the usual ones. sklearn.ensemble contains Extra Tree Classifer classification model. Here sklearn.dataset is used to import one classification based model dataset.
Step 2 - Setup the Data
X,y=load_iris(return_X_y=True)
print(X)
print(y)
Here, we have used load_iris function to import our dataset in two list form (X and y) and therefore kept return_X_y to be True.
Now our dataset is ready
Step 3 - Building the model
Before we do that, let's look at the important parameters that we need to pass.
1) n_estimators
It decides the number of trees in the forest.
2) criterion
The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain.
3) max_features
It decides the number of features to consider when looking for the best split.
Now that we understand, let's create the object
extra_tree_forest = ExtraTreesClassifier(n_estimators = 5,criterion ='entropy', max_features = 2)
- As you can see, we have set n_estimator to be 5
- Criterion is set to be entropy
- Max features is set here to be 2
Step 4 - Fit the model and find results
extra_tree_forest.fit(X, y)
feature_importance = extra_tree_forest.feature_importances_
print(feature_importance)
Here we have simply fit used fit function to fit our model on X and y. There after, we are trying to understand the importance of each feature based on model we built.
Step 5 - Lets look at our dataset now
Once we run the above code snippet, we will see:
Scroll down the ipython file to have a look at the results.
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