Wednesday 21 February 2024

An Overview of Supervised Machine Learning Models

 Supervised learning in machine learning is an algorithm attempting to model a target variable based on provided input data. The algorithm is provided with a collection of training data that includes labels. The program will derive a rule from a large dataset to predict labels for fresh observations. Supervised learning algorithms are given historical data and tasked with identifying the most effective predicting association.

Supervised learning algorithms may be categorized into two types: regression and classification algorithms. Supervised learning approaches based on regression aim to forecast outcomes using input variables. Classification-based supervised learning approaches determine the category to which a batch of data objects belongs. Classification algorithms are based on probabilities, determining the category with the highest chance that the dataset belongs to it. Regression algorithms predict the result of issues with a continuous collection of potential outcomes.

Academic and industrial researchers have utilized regression-based algorithms to create several asset pricing models. These models are utilized to forecast returns over different time frames and to pinpoint important characteristics that influence asset returns. Regression-based supervised learning has several applications in portfolio management and derivatives pricing.

Classification algorithms have been utilized in several financial domains to anticipate categorical outcomes. These encompass fraud detection, default prediction, credit rating, directional asset price movement projection, and Buy/Sell advice. Classification-based supervised learning is utilized in several applications within portfolio management and algorithmic trading.

An Overview of Supervised Learning Models

Classification predictive modeling involves predicting discrete class labels, while regression predictive modeling involves predicting continuous quantities. Both models use established factors to predict outcomes and have a substantial amount of similarities.

Some models can serve for both classification and regression with little adjustments. The approaches mentioned are K-nearest neighbors, decision trees, support vector machines, ensemble bagging/boosting methods, and artificial neural networks (including deep neural networks). Some models, including linear regression and logistic regression, are not suitable for both sorts of problems.


                                                  Fig:  Models for regression and classification

The algorithms are designed to learn from data, and they are often used to predict the outcome of a task. The two main categories of supervised learning are:

  • Regression: This is used for predicting continuous outputs, like weather forecasting or house price prediction.
  • Classification: This is used for predicting discrete outputs, like spam filtering or image recognition.

The following are different categories of both

  • Linear regression: This is a simple algorithm that learns a linear relationship between the input features and the output. It is a good choice for problems where the relationship between the input and output is well-understood.
  • Logistic regression: This is a variant of linear regression that is used for classification problems. It learns a linear relationship between the input features and the probability of a particular class.
  • Decision trees: These algorithms learn a tree-like structure that represents the decision process for making a prediction. They are easy to interpret and can be used for both regression and classification problems.
  • Random forests: These are ensembles of decision trees, which means that they combine the predictions of multiple decision trees to make a final prediction. They are often more accurate than individual decision trees and are also less prone to overfitting.
  • Support vector machines (SVMs): These algorithms learn a hyperplane that separates the data points into different classes. They are good for high-dimensional data and can be used for both regression and classification problems.
  • Naive Bayes: This is a simple algorithm that uses Bayes' theorem to make predictions. It is a good choice for problems where the features are independent of each other.
  • K-nearest neighbors (KNN): This algorithm classifies data points based on the labels of their nearest neighbors. It is a simple and easy to implement algorithm, but it can be slow and computationally expensive for large datasets.
  • Neural networks: These are complex algorithms that are inspired by the structure of the human brain. They can learn complex relationships between the input and output data and are often used for image recognition, natural language processing, and other challenging tasks.


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