Software engineering

FACULTY OF INTELLIGENT SYSTEMS AND COMPUTER
SCIENCE
"SOFTWARE ENGINEERING" DEPARTMENT
70610701 - "ARTIFICIAL INTELLIGENCE" SPECIALTY
202 - GROUP MASTER’S STUDENT
SHAHNOZA XAFIZOVA
From " Machine learning ".
INDEPENDENT WORK
Theme: The relationship between the potential function method and its
linear classification model
Samarkand 2022
There are two varieties of supervised learning algorithms: regression and classification algorithms. Regression-based supervised learning methods try to predict outputs based on input variables. Classification-based supervised learning methods identify which category a set of data items belongs to. Classification algorithms are probability-based, meaning the outcome is the category for which the algorithm finds the highest probability that the dataset belongs to it. Regression algorithms, in contrast, estimate the outcome of problems that have an infinite number of solutions (continuous set of possible outcomes).
In the context of finance, supervised learning models represent one of the most-used class of machine learning models. Many algorithms that are widely applied in algorithmic trading rely on supervised learning models because they can be efficiently trained, they are relatively robust to noisy financial data, and they have strong links to the theory of finance.
Regression-based algorithms have been leveraged by academic and industry researchers to develop numerous asset pricing models. These models are used to predict returns over various time periods and to identify significant factors that drive asset returns. There are many other use cases of regression-based supervised learning in portfolio management and derivatives pricing.
Classification-based algorithms, on the other hand, have been leveraged across many areas within finance that require predicting a categorical response. These include fraud detection, default prediction, credit scoring, directional forecast of asset price movement, and Buy/Sell recommendations. There are many other use cases of classification-based supervised learning in portfolio management and algorithmic trading.
Many use cases of regression-based and classification-based supervised machine learning are presented in Chapters 5 and 6.
Python and its libraries provide methods and ways to implement these supervised learning models in few lines of code. Some of these libraries were covered in Chapter 2. With easy-to-use machine learning libraries like Scikit-learn and Keras, it is straightforward to fit different machine learning models on a given predictive modeling dataset.
In this chapter, we present a high-level overview of supervised learning models. For a thorough coverage of the topics, the reader is referred to Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition, by Aurelien Geron (O’Reilly).
The following topics are covered in this chapter:
• Basic concepts of supervised learning models (both regression and classification).
• How to implement different supervised learning models in Python.
• How to tune the models and identify the optimal parameters of the models using grid search.
• Overfitting versus underfitting and bias versus variance.
• Strengths and weaknesses of several supervised learning models.
• How to use ensemble models, ANN, and deep learning models for both regression and classification.
• How to select a model on the basis of several factors, including model performance.
• Evaluation metrics for classification and regression models.
• How to perform cross validation.
Supervised Learning Models: An Overview
Classification predictive modeling problems are different from regression predictive modeling problems, as classification is the task of predicting a discrete class label and regression is the task of predicting a continuous quantity. However, both share the same concept of utilizing known variables to make predictions, and there is a significant overlap between the two models. Hence, the models for classification and regression are presented together in this chapter. Figure 4¬1 summarizes the list of the models commonly used for classification and regression.
Some models can be used for both classification and regression with small modifications. These are K-nearest neighbors, decision trees, support vector, ensemble bagging/boosting methods, and ANNs (including deep neural networks), as shown in Figure 4-1. However, some models, such as linear regression and logistic regression, cannot (or cannot easily) be used for both problem types.