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
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.
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.