Short recipe for building a machine learning classifier (Python)
I want to show you a short recipe for building a machine learning classifier in Python. Sometimes all you need is a way to quickly test for relationships in your data or to assess new features in a model. Here is a convenient way to do this.
First, import the necessary functions and classes from scikit-learn as shown here.
Second, get your data ready; meaning into numeric X, y vector format. Here, I am using the standard iris data set for demonstration
Third, train/test split your data using scikit’s standard utility function.
Now, train your model using a RandomForestClassifier with default hyperparameters. You can tweak the hyperparameters later as needed to improve your model.
Finally, assess your model’s performance by computing test score and confusion matrix.
Hope you find this helpful. Now you have a neat way of building classifiers in your coding toolbox.
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