foreword
There are three main categories of machine learning: supervised learning, unsupervised learning, and reinforcement learning. This article mainly introduces the principle, algorithm and application of supervised learning (Supervised Learning).
Article Directory
1. Principle
Supervised learning is one of the most common and widely used methods in machine learning. It is a method of predicting a target variable from a training dataset where each training sample has a known label or output value.
The goal of supervised learning is to build a predictive model by learning the relationship between input variables and output variables. This model can be used to predict or classify new input data.
The core idea of supervised learning is to learn a function or model through the samples in the training data set, so that the output value can be accurately predicted for unknown input data.
Supervised learning can be divided into two classes of problems: regression and classification. A regression problem is to predict the value of a continuous variable, such as house price prediction; a classification problem is to predict the value of a discrete variable, such as spam classification.
2. Algorithm
There are many classic algorithms in supervised learning. Here are some common algorithms:
1️⃣Linear regression
Linear regression is an algorithm used to solve regression problems. It predicts the value of a continuous variable by fitting the best straight line or hyperplane based on the linear relationship between the input variable and the output variable. The goal of linear regression is to minimize the error between the predicted value and the true value.
2️⃣Logistic regression
Logistic regression is an algorithm used to solve classification problems. It predicts the value of a discrete variable by mapping the input variable to a probability value. Logistic regression uses a logistic function such as the sigmoid function to establish the relationship between input and output variables.
3️⃣Decision tree
A decision tree is an algorithm used to solve classification and regression problems. It makes decisions by building a tree structure. Each internal node represents a feature or attribute, and each leaf node represents a category or value. The decision tree divides the input data and gradually determines which category or value it belongs to.
4️⃣Support Vector Machine
A support vector machine is an algorithm used to solve classification and regression problems. It performs classification or regression by finding an optimal hyperplane in the input space. The goal of a support vector machine is to find a hyperplane that maximizes the classification margin.
5️⃣ Random Forest
Random Forest is an ensemble learning algorithm for solving classification and regression problems. It makes predictions by building multiple decision trees and ensemble their results. Random forests are trained by randomly selecting features and samples to reduce the risk of overfitting.
3. Application fields
Supervised learning has a wide range of applications in various fields. Here are some common application areas:
1️⃣Natural Language Processing
In the field of natural language processing, supervised learning is used for tasks such as text classification, sentiment analysis, and named entity recognition. By training on labeled text data, a model for classifying or analyzing new text can be constructed.
2️⃣Image recognition
In the field of image recognition, supervised learning is used for tasks such as object recognition, face recognition, and image classification. By training on labeled image data, a model for recognizing or classifying new images can be constructed.
3️⃣Financial Forecast
In finance, supervised learning is used for tasks such as stock price prediction, credit scoring, fraud detection, and more. By training on historical data, a model that predicts future trends can be constructed.
4️⃣Medical diagnosis
In the medical field, supervised learning is used for tasks such as disease diagnosis, drug efficacy prediction, and genome analysis. By training on known medical data, it is possible to construct a model for diagnosing or predicting new patients.
Four. Summary
Supervised learning is an important machine learning method that builds a predictive model by learning the relationship between input variables and output variables. By learning from known training data, it is possible to accurately predict and classify unknown data.
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