pvhoang51 / perceptron

Example of training a perceptron via scikit-learn (using Iris dataset)

Machine Learning Classifiers Using Scikit-learn

Choosing a classification algorithm

The five main steps that are involved in training a machine learning algorithm can be summarized as follows:

• Selection of features
• Choosing a performance metric
• Choosing a classifier and optimization algorithm
• Evaluating the performance of the model
• Tuning the algorithm

First steps with scikit-learn

Related learning algorithms for classification: the perceptron rule and Adaline. The scikit-learn library offers not only a large variety of learning algorithms, but also many convenient functions to preprocess data and to fine-tune and evaluate our models.

Training a perceptron via scikit-learn (using Iris dataset)

Note: The perceptron algorithm never converges on datasets that aren't perfectly linearly separable, which is why the use of the perceptron algorithm is typically not recommended in practice.

Soucre: Python Maching Learning