Machine learning is a technology in the field of artificial intelligence. Its core idea is to use data to build a model that can learn independently and adapt to different environments. Different from traditional programming, the machine learning model does not need to manually give the steps and rules to solve the problem explicitly, but automatically extracts the features and rules by learning the patterns and rules in the data, and uses them to predict and predict unknown data. Classification.
Machine learning is divided into supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning, among which supervised learning is the most commonly used one. Supervised learning learns a function from labeled data to predict and classify unlabeled data. Common supervised learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, naive Bayes, etc.
Unsupervised learning does not require labeled data, but automatically learns the structure and characteristics of data from unlabeled data to achieve clustering, dimensionality reduction and other tasks. Common unsupervised learning algorithms include k-means, hierarchical clustering, principal component analysis, etc.
Semi-supervised learning is a learning method between supervised learning and unsupervised learning. It uses a small amount of labeled data and a large amount of unlabeled data to improve the generalization ability and performance of the model.
Reinforcement learning is a learning method based on the interaction between the agent and the environment. The agent learns the optimal strategy by interacting with the environment to obtain the maximum reward. Reinforcement learning has a wide range of applications in games, autonomous driving, robotics and other fields.
The principle, application, advantages and disadvantages of each algorithm are described in detail below:
Linear regression is a commonly used machine learning algorithm, mainly used to solve regression problems. Regression problems refer to the problems of predicting continuous numerical variables, such as predicting house prices, sales, etc. Linear regression predicts the value of the target variable by constructing a linear model. The principle, application, advantages and disadvantages of linear regression are introduced in detail below.
1 Linear regression
1.1 Principle of linear regression
The core of linear regression is to build a linear model, that is, assuming that there is a linear relationship between the target variable y and the independent variable x, expressed by the linear equation y = w1x1 + w2x2 + ... + wnxn + b, where w1, w2, .. ., wn is the coefficient of the independent variable, b is the intercept. The goal of linear regression is to find the optimal coefficient and intercept to minimize the error between the predicted value of the model and the real value. The commonly used error indicator is the mean square error (MSE).
In order to find the optimal coefficients and intercepts, the model needs to be trained with training data. During the training process, the model continuously adjusts the coefficients and intercepts through methods such as gradient descent, so that the error is continuously reduced, and finally the optimal model is obtained.
1.2 Application
Linear regression is widely used in various fields such as economics, statistics, physics, finance, etc. The following are some typical application scenarios: Predicting housing prices: Predict housing prices based on factors such as historical housing prices, area, and location.
Forecast sales: predict future sales based on factors such as historical sales, advertising investment, and promotional activities.
Analyzing ROI: Analyze the relationship between ROI and other factors through historical ROI, market indices, stock prices and other factors.
Forecast temperature, rainfall and other weather data: use historical weather data, temperature, humidity and other factors to predict future temperature and rainfall.
Analyze medical data: Analyze the relationship between diseases and other factors based on factors such as patient age, severity of illness, and medication status.
1.3 Advantages and disadvantages
The advantages of linear regression are:
Easy to use and fast calculation.
It has strong interpretability and can intuitively explain the relationship between variables.
The predictive performance is better when the feature space is small.
The disadvantages of linear regression are:
For data with nonlinear relationships, linear regression has weak predictive power.
Sensitive to outliers, which may affect the predictive ability of the model.
For data with a large feature space, overfitting problems are prone to occur.
Linear regression is less predictive of the noise and uncertainty present in the data.
In order to solve these problems, researchers have proposed some improved linear regression algorithms, such as ridge regression, Lasso regression, elastic network, etc. These algorithms improve the prediction performance and generalization ability of the model by adding regularization items and changing the loss function.
Although linear regression is simple, it still has a wide range of application scenarios in practical applications. For data with a small feature space and a relatively linear relationship, linear regression is a reliable predictive model.
2 Logistic regression
Logistic Regression is a machine learning algorithm widely used in classification problems. It can be used for binary classification problems and multi-classification problems, and is a common classification algorithm.
The core idea of logistic regression is to establish a functional relationship between the input features and the output probability, and use this function to classify the samples. In the binary classification problem, the output of logistic regression is a probability value between 0 and 1, indicating the probability that the sample belongs to the positive example, so the sample can be divided into two categories according to the set threshold. In multi-class classification problems, logistic regression can use the softmax function to normalize multiple probability values and output a probability distribution for each class.
Logistic regression uses the sigmoid function as the activation function. After the input features are linearly weighted and summed, the sigmoid function is converted into a probability value between 0 and 1. The loss function used in logistic regression is cross entropy (Cross Entropy), and its goal is to minimize the gap between the predicted value and the true label. Among them, p represents the probability distribution of the true label, and q represents the probability distribution of the predicted value. The smaller the value of cross entropy, the smaller the gap between the predicted value and the real label, and the better the performance of the model.
Advantages of logistic regression include:
The algorithm is simple, easy to understand and implement.
The training speed is fast and suitable for large-scale data sets.
The output probabilities can be used to predict the probability distribution of samples belonging to different classes.
Disadvantages of logistic regression include:
Works well for linearly separable data, but does not perform well for non-linearly separable data.
Sensitive to outliers.
In the case of unbalanced samples, it is easy to have the problem that the prediction results are biased towards the category with a large number of samples.
There may be an overfitting problem.
Logistic regression is a simple, effective classification algorithm that is applicable to many different data scenarios. In practice, we can improve the performance of the model by adjusting model parameters, feature engineering, regularization, etc., and combine other algorithms such as SVM and NN for comprehensive application to achieve better classification results.
3 Decision tree
Decision tree algorithm is a classification and regression algorithm based on tree structure. Its basic idea is to construct a tree structure by learning the training data, and classify or regression predict new samples. The construction process of the decision tree is based on the idea of "divide and conquer", which divides the data set according to certain characteristics, and repeats this process recursively on each split subset until each subset contains only data of the same category or reaches There are predefined stopping conditions. In the construction process, the decision tree algorithm uses certain indicators to measure the importance of features, so as to select the optimal partition features. Commonly used metrics include information gain, information gain ratio, and Gini index.
Application :
Decision tree algorithms can be applied to both classification and regression tasks. In a classification task, a decision tree outputs the category to which a sample belongs; in a regression task, a decision tree outputs a value representing a prediction for a new sample. Decision tree algorithms are widely used in data analysis and prediction tasks in various fields, including medicine, finance, telecommunications, e-commerce, etc. For example, in the medical field, decision tree algorithms can be used for tasks such as diagnosing diseases and predicting conditions; in the field of e-commerce, decision tree algorithms can be used for tasks such as predicting user purchase behavior.
Advantages and disadvantages :
advantage:
(1) Easy to understand and explain. Decision trees can be visualized, easy to understand and explain, and have good interpretability.
(2) Less data requirements. Decision tree algorithms can handle different types of data such as numeric, nominal, ordinal, etc.
(3) Handle missing values and outliers. The decision tree algorithm can handle missing values and outliers, making the model more robust.
(4) Efficient. The decision tree algorithm can efficiently process a large amount of data, and can dynamically add and delete samples through incremental training.
shortcoming:
(1) It is easy to overfit. When the depth of the decision tree is too large, it is prone to overfitting. It can be solved by pruning, setting the minimum number of samples of leaf nodes, etc.
(2) Unstable. When the data changes, the decision tree may be rebuilt, making the model unstable.
(3) It is difficult to deal with continuous data. For continuous data, the data needs to be discretized, which may lead to information loss.
(4) The correlation between variables is ignored. When building a decision tree, the decision tree algorithm usually only considers the influence of each variable on the target variable, ignoring the correlation between variables.
(5) Sometimes not precise enough. When dealing with complex problems, the decision tree algorithm sometimes cannot reach the optimal solution.
Decision tree algorithm is a classic machine learning algorithm, which has the advantages of easy understanding and interpretation, less requirements for data, handling missing values and outliers, and high efficiency. It can be applied to classification and regression tasks, and is widely used in data analysis and prediction tasks in various fields. However, the decision tree algorithm also has shortcomings such as easy overfitting, instability, difficulty in handling continuous data, ignoring the correlation between variables, and sometimes not accurate enough. When using the decision tree algorithm, it is necessary to select appropriate indicators and parameters according to specific application scenarios and data characteristics to achieve the best model effect.
4 Random Forest
Random Forest is an ensemble learning algorithm based on decision trees. In the process of constructing the decision tree, the random forest adopts the method of self-sampling (Bootstrap Sampling) and the method of randomly selecting features to improve the generalization ability and anti-noise ability of the model. When predicting, random forest will integrate the results of multiple decision trees to obtain more accurate prediction results.
The construction process of random forest is as follows:
(1) From the training set, use the self-service sampling method to randomly sample several training subsets.
(2) For each training subset, a decision tree is constructed by randomly selecting features.
(3) Repeat steps (1) and (2) multiple times to obtain multiple decision trees.
(4) For a new sample, the set of prediction results of multiple decision trees is obtained by voting, which is used as the final prediction result.
Algorithm application:
Random forest algorithm can be used for both classification and regression tasks. In classification problems, random forests can be used to recognize handwritten characters, predict stock prices, and more. In regression problems, random forests can be used to predict house prices, sales volume, etc.
Algorithm pros and cons :
The random forest algorithm has the following advantages:
(1) It can handle large-scale data sets and has good generalization performance.
(2) Missing values and outliers can be automatically handled.
(3) It can handle high-dimensional data and does not require feature selection.
(4) The importance of each feature can be evaluated, which is conducive to feature engineering.
The random forest algorithm also has the following disadvantages:
(1) The computational complexity is high, and it needs to consume a lot of computing resources during training.
(2) The model is large and requires a large amount of memory for storage and calculation.
(3) Due to the introduction of randomness, the interpretability of the model is poor.
(4) For nonlinear data sets, random forest may not be able to achieve the best results.
Random forest is an ensemble learning algorithm based on decision trees, which has the advantages of processing large-scale data sets, automatically handling missing values and outliers, being able to handle high-dimensional data, and evaluating the importance of each feature. However, random forests also have high computational complexity, large models, and poor interpretability.
5 Support Vector Machines
Support Vector Machine (SVM) is a commonly used supervised learning algorithm, mainly used for classification and regression problems. The basic idea of SVM is to construct a hyperplane in a high-dimensional space, separate data points of different categories, and maximize the interval to make the classification results more robust.
Application of SVM algorithm:
Classification problems: SVM can handle binary and multi-classification problems. Common applications include spam classification, image classification, text classification, etc.
Regression problems: SVM can also be used for regression problems and is often called Support Vector Regression (SVR). For example, SVR can be used to predict continuous variables such as housing prices or stock prices.
Advantages and disadvantages of SVM algorithm:
advantage:
The SVM algorithm works well for small sample and high-dimensional data processing;
Since the SVM algorithm is based on structural risk minimization, it can effectively avoid overfitting;
The SVM algorithm can use the kernel function to realize nonlinear classification and regression, making it more adaptable;
The SVM algorithm can improve the classification accuracy of the algorithm by adjusting the kernel function and parameters.
shortcoming:
The SVM algorithm takes a long time to train for large-scale data sets and requires a large storage space;
The SVM algorithm is sensitive to the selection of kernel functions for nonlinear problems, and different kernel functions may lead to different classification effects;
The SVM algorithm has poor fault tolerance for noisy data and missing data, and data preprocessing is required;
The model of the SVM algorithm is not easy to explain, and the contribution of the model to the classification cannot be intuitively drawn.
6K neighbors
K-Nearest Neighbor (KNN) is a commonly used parameter-free supervised learning algorithm, mainly used for classification and regression problems. The basic idea of the KNN algorithm is to find the K training samples closest to the sample to be predicted in the training set, and predict the label of the sample to be predicted through the labels of the K training samples.
Application of KNN algorithm :
Classification problems: KNN can handle binary and multi-classification problems. Common applications include image classification, text classification, etc.
Regression problems: KNN can also be used in regression problems to predict the value of continuous variables by predicting the average or weighted average of neighbor labels. For example, KNN can be used to predict continuous variables such as housing prices or stock prices.
Advantages and disadvantages of the KNN algorithm :
advantage:
The KNN algorithm is easy to implement and does not need to assume the distribution of data;
The KNN algorithm can get good results for both linear and nonlinear problems;
The KNN algorithm has less influence on outliers and noise data;
The KNN algorithm has better expressive power for the local structure in the data set.
shortcoming:
The KNN algorithm needs to maintain all training data, so for large-scale data sets, the storage and computing overhead is relatively large;
The KNN algorithm is sensitive to the dimension of the input data. When the data dimension is high, it is prone to the "dimension disaster" problem;
The KNN algorithm needs to determine the K value in advance, and the selection of the K value has a great impact on the classification results;
The KNN algorithm may have problems with too high or too low model complexity near the classification boundary.
The principle of the KNN algorithm is relatively simple , and its basic idea is to find the K training samples closest to the sample to be predicted by calculating the distance between the sample to be predicted and all samples in the training set. Then use the labels of the K training samples to predict the labels of the samples to be predicted.
Specifically, the KNN algorithm can be divided into the following steps:
1 Calculate the distance between the sample to be predicted and all samples in the training set. Common distance metrics include Euclidean distance, Manhattan distance, Minkowski distance, etc.
2 According to the size of the distance, select K training samples closest to the sample to be predicted.
3 For the classification problem, predict the label of the sample to be predicted through the labels of K training samples. Common methods include voting methods and weighted voting methods. For regression problems, the value of the continuous variable is predicted by the average or weighted average of the labels of the K training samples.
It should be noted that the KNN algorithm is sensitive to the dimension of the input data. When the data dimension is high, there will be a "dimension disaster" problem, because the data samples in the high-dimensional space are likely to have equal or similar distances, resulting in the KNN algorithm. accuracy drops. In addition, the KNN algorithm needs to determine the K value in advance. The selection of the K value has a great impact on the classification results, and needs to be tuned through cross-validation and other methods.
7 Naive Bayes
Naive Bayesian algorithm is a classification algorithm based on Bayesian theorem. Bayes' theorem is an important theorem in probability theory, which describes the probability of another event occurring under the condition that another event is known. In the classification problem, the Naive Bayesian algorithm calculates the probability that the sample to be classified belongs to each category according to the conditional probability of each feature in the training data for classification, and then selects the category with the highest probability as the classification result.
The basic assumption of the Naive Bayesian algorithm is that each feature is independent of each other in the classification, that is, it is assumed that the value of a feature is not affected by other features. Although this assumption may not be true in practice, in many cases, the effect of the Naive Bayesian algorithm is relatively good.
Specifically, the Naive Bayes algorithm can be divided into the following steps:
1 Calculate the probability of occurrence of each category based on the training data, that is, the prior probability.
2 Calculate the probability of each feature appearing in each category based on the training data, that is, the conditional probability. Common calculation methods include maximum likelihood estimation and Bayesian estimation.
3 For the samples to be classified, calculate the probability of belonging to each category according to the conditional probability, and select the category with the highest probability as the classification result.
It should be noted that the implementation of the Naive Bayesian algorithm needs to consider the discretization of the data. If the data is continuous, it needs to be discretized first. In addition, if a certain feature has not appeared in the training data, the conditional probability calculation result will be 0. In this case, smoothing techniques need to be used for processing. Commonly used smoothing methods include Laplace smoothing and Bayesian smoothing wait.
Advantages of the Naive Bayes algorithm include:
The calculation is simple and fast, and it is suitable for processing high-dimensional data.
Performs well for small sample data.
Can handle multi-classification problems.
Insensitive to noisy data.
Disadvantages of the Naive Bayes algorithm include:
The requirement for the independence of the input features is high, and if the correlation between the features is strong, it will affect the classification effect.
It is difficult to deal with missing data.
It is sensitive to data with wrong class labels.
It should be noted that although the Naive Bayesian algorithm performs well in many cases, its classification results may not be optimal, especially in the case of strong correlation between features. Therefore, it is necessary to select an appropriate classification algorithm according to the actual situation in specific applications.
8 k-means
The k-means algorithm is a common clustering algorithm. Its goal is to divide a set of data into k clusters, so that the data similarity in the same cluster is high, while the similarity between different clusters is low. The principle, application, advantages and disadvantages of the k-means algorithm will be introduced below.
principle
The principle of the k-means algorithm is relatively simple, and its specific steps are as follows:
1 Initialization: First randomly select k points as the initial centroid.
2 Clustering: Cluster all data points according to the nearest centroid to form k clusters.
3 Recalculate the centroid: For each cluster, recalculate its centroid, that is, take the mean of all its points.
4. Judging whether it is converged: if the distance between the current centroid and the previous round of centroid is less than a certain threshold, the algorithm is considered converged and the final result is output; otherwise, the current centroid is used as the new centroid and returns to step 2 to continue iteration.
application
The k-means algorithm is widely used in data mining, image processing, natural language processing and other fields, for example:
Market analysis: By clustering consumer data, we can understand the needs and consumption behavior of different groups of people, which will help to formulate more accurate market strategies.
Image segmentation: Divide the pixels in the image into different regions according to the similarity to realize image segmentation.
Gene clustering: By clustering gene expression data, find out the gene set related to a certain disease, which is helpful for the research and treatment of the disease.
Advantages and disadvantages
The advantages of the k-means algorithm include :
The algorithm is simple, easy to understand and implement.
It is suitable for large-scale data sets and has a fast calculation speed.
The clustering effect is better, especially for data with uniform distribution and similar density.
Disadvantages of the k-means algorithm include :
The number k of clusters needs to be specified in advance, and has a great impact on the result.
Clustering may not work well for data of different densities and shapes.
The initial centroid is randomly selected, which may cause the algorithm to fall into a local optimal solution.
9 Hierarchical Clustering
The Hierarchical Clustering algorithm is a tree-based clustering method. Its main idea is to build a clustering tree by continuously merging or splitting clusters until all data points are in the same cluster. until clustered.
Hierarchical clustering can be divided into two categories, one is top-down (Top-Down) partition clustering, and the other is bottom-up (Bottom-Up) agglomerative clustering. Top-down clustering needs to determine an overall cluster, and then gradually subdivide into small clusters; bottom-up agglomerative clustering starts with a single data point and gradually merges similar data points into a cluster.
The main steps of hierarchical clustering are as follows:
Treat each sample as an initial cluster;
Calculate the distance or similarity between all clusters, and select the two clusters with the closest distance or the highest similarity to merge into a new cluster;
Repeat step 2 until all data points are in the same cluster, or reach the preset number of clusters.
The advantages and disadvantages of hierarchical clustering are as follows:
advantage:
Hierarchical clustering does not need to specify the number of clusters in advance, and can adaptively select the number of clusters according to needs;
Hierarchical clustering can visualize the data, and by drawing the graph of the clustering tree, you can better understand the relationship between the data;
Hierarchical clustering can use different distance measurement methods and similarity measurement methods to adapt to different data types and application scenarios.
shortcoming:
The computational complexity of hierarchical clustering is high, and the processing of large-scale data requires a lot of time and computing resources;
Hierarchical clustering is sensitive to noise and outliers, which may lead to instability of clustering results;
The division result of hierarchical clustering is irreversible. Once the clustering result is determined, the result cannot be modified or adjusted.
10 GBDT
The GBDT (Gradient Boosting Decision Tree) algorithm is an integrated learning algorithm based on decision trees. It is an important representative under the Boosting framework. Due to its excellent performance, it has been widely used in data mining, statistical learning and other fields. The principle, application, advantages and disadvantages of the GBDT algorithm will be introduced in detail below.
principle
The GBDT algorithm combines multiple weak classifiers (decision trees) into one strong classifier. Its core idea is to build a new decision tree for the misclassified samples in the training set to make up for the defects of the previous model. The new model is learned based on the residual error of the original model, that is, the gradient descent of the error is used to train the model. Through continuous iteration, the prediction results of each decision tree are accumulated, and the final prediction result is the weighted sum of multiple decision trees. It should be noted in the whole process that the construction of each decision tree is based on the residuals of the previously constructed decision trees, so they are related.
application
The GBDT algorithm has applications in many fields, such as:
Financial risk control: through the analysis of customer data, build a GBDT model to score customers, so as to predict and control risks;
Recommendation system: Build a GBDT model based on user historical data to predict user behavior, such as whether the user will buy a certain product, so as to recommend more suitable products for the user;
Medical diagnosis: The patient's medical record data is used as input to construct a GBDT model to predict the disease, thereby assisting doctors in diagnosis and treatment;
Industrial manufacturing: Through the analysis of production data, build a GBDT model to optimize the production process, such as predicting the failure rate of equipment, thereby reducing downtime and losses.
Advantages and disadvantages
The G BDT algorithm has the following advantages :
Good performance for both classification and regression problems;
Can handle multiple types of data, including discrete and continuous;
Feature selection and feature combination can be performed automatically;
It is robust and can handle some noisy data.
But the GBDT algorithm also has some disadvantages :
The training time is long and multiple rounds of iterations are required;
Sensitive to outliers and noise;
It is easy to overfit and needs some tuning and optimization.
11 XGBoost
XGBoost is a decision tree-based ensemble learning algorithm that can be used for both classification and regression problems. It is an optimized version of the Gradient Boosting Decision Tree (GBDT) algorithm, which mainly solves the problems of slow calculation speed and overfitting of the GBDT algorithm when processing large-scale data.
The principle of the XGBoost algorithm is to realize integrated learning by building multiple decision trees. Each decision tree is split according to some features, and each leaf node corresponds to a prediction result. During the training process, the algorithm will adjust the structure and weight of each decision tree according to the performance of the current model to optimize the performance of the model.
The main applications of the XGBoost algorithm include:
Binary classification and multi-classification problems: XGBoost can be used for classification, and by feature extraction and classification of samples, the distinction and identification of different categories can be realized.
Regression problem: XGBoost can be used for regression, and the prediction of the target variable can be achieved by modeling and training the data.
The advantages of the XGBoost algorithm include:
Strong scalability: XGBoost algorithm can handle large-scale data sets, and for problems with massive data, distributed computing framework can be used for processing.
Strong robustness: XGBoost algorithm is robust to missing values and outliers, and can effectively deal with these problems.
High prediction accuracy: XGBoost algorithm can effectively deal with high-dimensional sparse data and has high prediction accuracy.
Disadvantages of the XGBoost algorithm include:
High requirements for computing resources: XGBoost algorithm requires a lot of computing resources, including CPU and memory resources.
The hyperparameter setting is more complicated: there are many hyperparameters in the XGBoost algorithm, which need to be set and adjusted reasonably.
12 LightGBM
LightGBM is a machine learning algorithm based on gradient boosting trees, and its full name is Light Gradient Boosting Machine. It was developed by Microsoft Corporation and is a fast and efficient gradient boosting framework. Compared with the traditional GBDT algorithm, LightGBM has higher training speed and lower memory consumption, while maintaining a high prediction accuracy.
The principle of LightGBM is based on the idea of decision tree integration, and the residual error is continuously fitted through multiple rounds of iterations, and finally a model with a high degree of fitting is obtained. The key technology is the adoption of two innovative optimization methods: histogram-based decision tree algorithm and mutually exclusive feature bundling.
LightGBM has a wide range of applications and can be used for classification and regression problems, as well as for application scenarios such as sorting and recommendation. It performs very well in some data-intensive tasks, such as natural language processing, image recognition and recommender systems.
The advantages of LightGBM include:
1. Efficiency: LightGBM adopts a multi-threaded parallel processing method, which speeds up the training process and reduces memory usage. It uses histograms to reduce the complexity of the decision tree, resulting in faster model building.
2. High precision: LightGBM uses multiple rounds of iterations in the training process of the model, and optimizes the performance of the model by continuously fitting the residual. In addition, LightGBM also supports categorical features and missing values, which improves the applicability of the model.
3. Good scalability: LightGBM can handle massive data sets, supports distributed computing and GPU acceleration, and can achieve fast training and prediction on large-scale data sets.
4. Interpretability: LightGBM can output an importance ranking for each feature, allowing users to better understand how the model works.
Disadvantages of LightGBM include:
1. Data needs to be preprocessed: LightGBM requires data to be preprocessed to convert categorical variables into numerical variables.
2. Parameters need to be adjusted: The performance of LightGBM largely depends on the settings of hyperparameters, which need to be adjusted reasonably.
3. Does not support online learning: LightGBM does not support incremental learning and needs to retrain the entire model.