See Part I to get insight on Supervised Learning.
In unsupervised learning, as the name suggests, the dataset used for training does not contain the required answers. Instead, the algorithm uses techniques such as clustering and dimensionality reduction to train.
A major application of unsupervised learning is anomaly detection. This uses a clustering algorithm to find out major outliers in a graph. These are used in credit card fraud detection.
Types of Supervised Models
Supervised models are trained on labeled dataset. It can either be a continuous label or categorical label.
Regression is used when one is dealing with continuous values such as the cost of a house when you are given features such as location, the area covered, historic prices etc. Popular regression models are:
- Linear Regression
- Lasso Regression
- Ridge Regression
Classification is used for data that is separated into categories with each category represented by a label. The training data must contain the labels and must have sufficient observations of each label so that the accuracy of the model is respectable. Some popular classification models include:
- Support Vector Classifiers
- Decision Trees
- Random Forests Classifiers
There are various evaluation methods to find out the accuracy of these models also. We will discuss these models, the evaluation methods and a technique to improve these models called hyperparameter tuning in greater detail.
Stay tuned for the next installment in this series to find out about the Types of Classification.
Visit QuantInsti for additional insight on this topic: https://blog.quantinsti.com/machine-learning-classification/
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