classification – Data Science and GIS

This post aims to share my Machine Learning notebooks. There are three types of Machine Learning for predicting structured tabular data: (1) supervised learning, (2) unsupervised learning, and (3) reinforcement learning. A supervised learning objective is to build a prediction model from a training dataset to predict an unseen test dataset. Supervised learning can solve regression tasks (for continuous output) and classification tasks (for categorical output). Unsupervised learning aims to learn the dataset patterns to simplify the information by clustering and dimensionality reduction. Cluster analysis groups observations into some clusters according to the similarity of their features. Dimensionality reduction reduces the number of dataset dimensions or features. Previously, I have written a post on basic Machine Learning here.

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The word “machine learning” sounds like a machine with robot appearance learning something. Actually, machine learning is very related to the user feeding large amount of training data into the machine to learn. The machine then will learn the pattern of the data and, as a result, can understand the data pattern and create a model. The model from machine learning basically can classify, cluster, and predict test data according to the training data.

There are three kinds of machine learning, supervised learning, unsupervised learning, and reinforcement learning. This article discusses supervised and unsupervised learning only. Supervised learning can classify or predict test data from labeled training data. Supervised learning learns the labels of training dataset to classify or predict new dataset according to the variables. Supervised learning can do classification and regression. If the label is categorical, it is called classification. If the label is continuous number, it called regression.

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Tag: classification

Machine Learning Notebooks Collection

Introduction to Machine Learning