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Machine Learning Classification Algorithms – Part I

Let me start by asking a very basic question. What is Machine LearningMachine learning is the process of teaching a computer system certain algorithms that can improve themselves with experience.

A very technical definition would be,

“A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience.” –  Tom Mitchell, 1997

Just like humans, the system will be able to perform simple classification tasks and complex mathematical computations like regression. It involves the building of mathematical models that are used in classification or regression.

To ‘train’ these mathematical models, you need a set of training data. This is the dataset over which the system builds the model. This article will cover all your Machine Learning Classification needs, starting with the very basics.

Here, we will talk about:

  • Supervised Learning
  • Unsupervised Learning
  • Types of Supervised models
  • Types of classification
  • Classifier Models
  • Hyperparameter Tuning
  • Performance Evaluation
  • Using SVC for Trading
  • Resources to learn Machine Learning
  • Summary

The mathematical models are divided into two categories, depending on their training data – supervised and unsupervised learning models.

Supervised Learning

When building supervised learning models, the training data used contains the required answers. These required answers are called labels. For example, you show a picture of a dog and also label it as a dog.

So, with enough pictures of a dog, the algorithm will be able to classify an image of a dog correctly. Supervised learning models can also be used to anticipate continuous numeric values such as the price of the stock of a certain company. These models are known as regression models.

In this case, the labels would be the price of the stock in the past. So the algorithm would follow that trend. Few popular algorithms include

  • Linear Regression
  • Support Vector Classifiers
  • Decision Trees
  • Random Forests

Stay tuned for the next installment in this series to learn about Unsupervised Learning.

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