Machine Learning for Beginners in Python: How to Select Important Features In Random Forest

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Select Important Features In Random Forest

Preliminaries


/* Load libraries */
from sklearn.ensemble import RandomForestClassifier
from sklearn import datasets
from sklearn.feature_selection import SelectFromModel

Load Iris Flower Data


/* Load data */
iris = datasets.load_iris()
X = iris.data
y = iris.target

Create Random Forest Classifier


/* Create random forest classifier */
clf = RandomForestClassifier(random_state=0, n_jobs=-1)

Select Features With Importance Greater Than Threshold

The higher the number, the more important the feature (all importance scores sum to one). By plotting these values we can add interpretability to our random forest models.


/* Create object that selects features with importance greater than or equal to a threshold */
selector = SelectFromModel(clf, threshold=0.3)

/* Feature new feature matrix using selector */
X_important = selector.fit_transform(X, y)

View Selected Important Features


/* View first five observations of the features */
X_important[0:5]
array([[ 1.4,  0.2],
       [ 1.4,  0.2],
       [ 1.3,  0.2],
       [ 1.5,  0.2],
       [ 1.4,  0.2]])

Train Model With Selected Important Features


/* Train random forest using most important features */
model = clf.fit(X_important, y)

 

Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

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