Table of contents
4.1. K-Nearest Neighbor Algorithm
4.3. Decision tree and random forest
5. Machine learning development process
1. What is machine learning
Machine Learning (ML) is a branch of Artificial Intelligence (AI) that allows computers to learn and improve from data without explicit programming instructions. The goal of machine learning is to enable computers to learn from experience and to improve their own performance through learning.
A traditional computer program is when a programmer writes explicit rules and instructions for a computer to perform a specific task. Whereas in machine learning, we provide data and corresponding outcomes (labels) to train a model, allowing the computer to learn regularities and patterns from the data and make predictions or decisions on new, unseen data.
Machine learning can be divided into three main types:
- Supervised Learning: In supervised learning, the model uses training data with labels (correct answers) to learn to predict the output on new unlabeled data. Common tasks include classification (e.g. image classification, spam detection, etc.) and regression (e.g. house price prediction).
- Unsupervised Learning: In unsupervised learning, the model learns using unlabeled data, with the goal of discovering patterns, structures, or features from the data. Common tasks include clustering (such as customer segmentation) and dimensionality reduction (such as data visualization).
- Reinforcement Learning: Reinforcement learning is a method for a model to learn the best behavior policy from trial and error. In reinforcement learning, a model learns behaviors that maximize cumulative rewards based on rewards and punishments by interacting with the environment.
Machine learning has a wide range of applications in many fields, such as natural language processing, computer vision, medical diagnosis, financial forecasting, etc. Through machine learning, computers can learn from data and make intelligent decisions, which makes it one of the core methods in modern artificial intelligence technology.
2. Dataset
Dataset composition refers to the composition and structure of datasets used in machine learning and data science tasks. A data set usually consists of three parts: training set, verification set and test set. The role of each part is explained below:
- Training Set: The training set is the data set that the machine learning model uses to learn and adjust parameters. During the training phase, the model uses the samples of the training set and the corresponding labels (or results) to learn the relationship and regularity between the data. The training set plays a key role in model training. The model minimizes the prediction error by continuously adjusting its own parameters, so that it performs well on the training data.
- Validation Set: A validation set is a data set used to adjust model hyperparameters (such as learning rate, model complexity, etc.) and select a model. During the training process, the parameters obtained by the model on the training set may be overfitting on the test set (Overfitting), so it is necessary to use the verification set to evaluate the performance of the model on unseen data and select appropriate hyperparameters . Evaluation on the validation set allows the selection of the best performing model and avoids over-optimization on the test set.
- Test Set: The test set is the data set used to evaluate the performance of the model, which is the data that has never been used in the training process. The model makes predictions on the test set to evaluate its performance and generalization ability in real applications. The purpose of the test set is to simulate the performance of the model in the real environment, so the accuracy and representativeness of the test set are very important.
Precautions:
- Datasets should be randomly sampled from the population as much as possible to ensure representativeness of the sample.
- The division of the dataset should avoid data leakage, that is, ensure that there are no overlapping samples between the test set and the training set.
- The size and quality of the dataset have a significant impact on the performance of the model, so high-quality data should be collected and cleaned as much as possible.
The composition and use of datasets are critical to the effectiveness of machine learning algorithms, so careful dataset partitioning and evaluation are required for practical applications.
2.1. Structure
The structure of the data set usually consists of feature values (Features) and target values (Target).
- Eigenvalues: Eigenvalues are the input data in a dataset, also known as independent variables or attributes. Each sample has a set of eigenvalues that describe the characteristics and properties of that sample. Feature values can be different types of data such as numerical values, categories, texts, etc., and they are used to represent various aspects of the sample.
- Target value: The target value is the output of the machine learning task, also known as the dependent variable or label. In supervised learning tasks, the target value is known and used to train the model. A model makes predictions or classifies unseen data by learning the relationship between feature values and target values.
For example, in a housing price prediction task, the feature values may include the area of the house, the number of rooms, the location, etc., and the target value is the actual selling price of the corresponding house. The model predicts the selling price of other unknown houses by learning the relationship between feature values and house prices.
The structure of a dataset can be represented as a table, where each row represents a sample, each column represents a feature or attribute, and the last column is the target value. This structure is widely used in supervised learning tasks, where the model uses feature values to predict target values.
In unsupervised learning tasks, datasets usually only contain feature values, since the goal of unsupervised learning is to discover patterns and structures in data without predefined target values.
3. Algorithm classification
Machine learning algorithms can be categorized based on how they learn and the type of task they do. The main classification methods include:
- Classified according to learning style:
-
- Supervised Learning: In supervised learning, an algorithm uses training data with labels (correct answers) to learn to predict the output on new unlabeled data. Common algorithms include linear regression, logistic regression, support vector machines, decision trees, random forests, and more.
- Unsupervised Learning: In unsupervised learning, algorithms learn using unlabeled data, with the goal of discovering patterns, structures, or features from the data. Common algorithms include clustering, dimensionality reduction, association rule mining, etc.
- Reinforcement Learning: Reinforcement learning is a method for an algorithm to learn the best behavior strategy from trial and error by interacting with the environment. Common applications include robot control, game strategy, and more.
- Classified by task type:
-
- Classification: The classification task is to divide samples into different categories or labels. Common algorithms include logistic regression, support vector machines, decision trees, etc.
- Regression: The regression task is to predict a continuous-valued output. Common algorithms include linear regression, ridge regression, Lasso regression, etc.
- Clustering: The clustering task is to divide samples into different groups (clusters), so that the similarity of samples in the same group is high, while the similarity between different groups is low. Common algorithms include K-means clustering, hierarchical clustering, etc.
- Dimensionality Reduction: The dimensionality reduction task is to reduce the feature dimension and retain the most important information in the data. Common algorithms include principal component analysis (PCA), t-SNE, etc.
- Association Rule Mining (Association Rule Mining): Association rule mining is the discovery of association patterns in data, and is used to discover frequent itemsets and rules in data. Common algorithms include Apriori algorithm, FP-Growth algorithm and so on.
In addition to the above classification methods, there are also specific types of algorithms such as Ensemble Learning, Deep Learning, and Transfer Learning, which are widely used in different scenarios and tasks.
4. Algorithm Introduction
The following algorithms are introduced in turn: k-nearest neighbor algorithm, Bayesian classification, decision tree and random forest, logistic regression, neural network, linear regression, ridge regression, clustering k-means:
- k-Nearest Neighbors (K-Nearest Neighbors, KNN for short):
-
- KNN is a basic supervised learning algorithm used for classification and regression tasks.
- For classification tasks, KNN predicts the label of a new sample based on the labels of the k samples by measuring the distance between the new sample and the nearest k samples in the training data.
- For regression tasks, KNN predicts the output value of a new sample based on the average or weighted average of the most recent k samples.
- Bayesian Classification:
-
- Bayesian classification is a probabilistic and statistical classification method based on Bayesian theorem.
- The algorithm assumes that the features are independent of each other, and uses Bayesian theorem to calculate the posterior probability of each category given the features, and then selects the category with the highest posterior probability as the prediction result.
- Decision Trees and Random Forests:
-
- A decision tree is a classification and regression algorithm based on a tree structure. It constructs a tree structure for prediction by segmenting features layer by layer.
- Random forest is an ensemble learning method based on the ensemble of multiple decision trees for classification or regression. It constructs multiple decision trees by randomly selecting features and samples, and finally votes or averages their results to obtain the final prediction result.
- Logistic Regression:
-
- Logistic regression is a type of linear model used to solve classification problems.
- It uses a logistic function (also known as the sigmoid function) to map a linear combination of features to a probability value between 0 and 1 for classification.
- Neural Networks:
-
- A neural network is a complex nonlinear model that mimics the way neurons in the human brain work.
- It consists of a hierarchy of neurons that process inputs through weights and activation functions to ultimately achieve classification, regression, or other tasks.
- Linear Regression (Linear Regression):
-
- Linear regression is a type of linear model used to solve regression problems.
- It fits the relationship between features and target values by finding the optimal linear relationship.
- Ridge Regression:
-
- Ridge regression is a regularized linear model for solving linear regression problems.
- It adds an L2 regularization term to linear regression to solve the problem of feature collinearity and avoid overfitting.
- Clustering k-means (K-Means Clustering):
-
- K-means is an unsupervised learning algorithm used to cluster data.
- It groups similar data points together by dividing the data into k clusters such that each data point belongs to the center point of the nearest cluster.
These algorithms are widely used in different problems and tasks, and each algorithm has its applicable scenarios and characteristics. Machine learning engineers and data scientists choose appropriate algorithms to solve practical problems based on the needs of the problem and the nature of the data.
4.1. K-Nearest Neighbor Algorithm
K-Nearest Neighbors (KNN for short) is a basic supervised learning algorithm that can be used for classification and regression tasks. The principle of the algorithm is very simple and intuitive, and it is applicable to problems in many different domains.
Algorithm principle : The KNN algorithm is based on the idea of "closer to vermilion is red, and closer to ink is black", that is, it is believed that the category or value of a sample will be affected by the nearest samples around it. The steps of the algorithm are as follows:
- Training phase: Store the labeled training data.
- Test phase: For a new unlabeled data point, calculate its distance from all data points in the training data (usually using Euclidean distance or Manhattan distance, etc.). Select the k training samples closest to the new data.
- Classification task: For the classification task, the most frequently occurring category among the k most recent samples is selected as the predicted label for the new data point.
- Regression task: For a regression task, the average or weighted average of the output values of these k most recent samples is selected as the predicted output value for the new data point.
Selection of parameter k : Parameter k represents the number of nearest neighbor samples considered in prediction, which usually needs to be set manually. Choosing an appropriate value of k is very important for algorithm performance. A smaller k value may cause the model to be sensitive to noise and be prone to overfitting; while a larger k value may cause the model to be too simple and prone to underfitting. Therefore, techniques such as cross-validation are usually used to select the best k value.
Advantages and disadvantages : The advantages of the KNN algorithm are that it is simple and easy to understand, easy to implement, and performs well on some simple problems. It does not require an explicit training process and is suitable for dynamic datasets. However, the disadvantage of KNN is the high computational complexity, especially on large-scale datasets, because the distance between each test sample and all training samples has to be calculated. In addition, KNN may perform poorly for high-dimensional data and data with inconsistent feature scales.
KNN is often used as a baseline model for learning algorithms to quickly understand data and problems. In practical applications, KNN is often used in combination with other algorithms, such as voting or weighted average to obtain better classification or regression results.
4.2, Bayesian classification
Bayesian classification (Bayesian Classification) is a classification method based on probability and statistics, which uses Bayesian theorem for classification. This algorithm works well in many practical applications, especially in fields such as text classification.
Algorithm principle : In Bayesian classification, we assume that each feature is independent of each other. Based on this assumption, we can use Bayes' theorem to calculate the posterior probability of each class given the features. Specific steps are as follows:
- Training phase: First, we need to learn the prior probability of each category (how often the category appears) and the conditional probability of each feature appearing in different categories from the labeled training data.
- Testing phase: For a new unlabeled data point, we calculate its posterior probability under each category, and then select the category with the highest posterior probability as the prediction result.
Bayes' theorem : Bayes' theorem is a fundamental formula in probability theory that is used to calculate conditional probabilities. For classification tasks, the expression of Bayes' theorem is as follows: P ( y ∣ x )= P ( x ) P ( x ∣ y )⋅ P ( y ) where, P ( y ∣ x ) represents the given feature x The posterior probability of belonging to category y; P ( x ∣ y ) represents the conditional probability of feature x appearing under category y; P ( y ) represents the prior probability of category y; P ( x ) represents the prior probability of feature x.
Pros and Cons : The advantages of Bayesian classifiers lie in their simplicity and efficiency. It performs well for small-scale datasets and can handle high-dimensional data with a large number of features. In addition, Bayesian classifiers are also robust to missing data.
However, the disadvantage of Bayesian classifiers is that it assumes that the features are independent of each other, which may not be true in some cases. Also, because Bayesian classifiers use probabilistic models, they can be less efficient when dealing with continuous features and larger datasets.
Bayesian classifiers are widely used in natural language processing (such as spam classification, text classification) and other scenarios, and it provides a simple and effective method for solving probabilistic classification problems.
4.3. Decision tree and random forest
Decision trees and random forests are two commonly used machine learning algorithms for solving classification and regression tasks. They are both based on decision tree models, but random forest is an ensemble learning method that improves predictive performance through the ensemble of multiple decision trees.
Decision Tree : A decision tree is a tree-based model used for classification and regression problems. It builds a tree structure by segmenting features layer by layer until a stopping condition is met or a maximum depth is reached. In a decision tree, each internal node represents a feature, and each leaf node represents a category (for classification problems) or a value (for regression problems).
The construction process of the decision tree is based on the greedy algorithm, which maximizes the information gain (or minimizes the impurity) by selecting the optimal features and segmentation points. Decision trees can be easily visualized and interpreted, but can be prone to overfitting the training data, especially when the depth of the tree is large.
Random Forest : Random Forest is an ensemble learning method that builds an ensemble of multiple decision trees for classification or regression. In random forests, each decision tree is trained on randomly selected features and samples. Specific steps are as follows:
- Random sampling: Randomly sample from the training data with replacement to generate multiple different training data sets.
- Decision tree construction: For each training dataset, build a decision tree. When constructing each node, a subset of features is randomly selected for splitting.
- Integration: For classification problems, the final category is determined by voting; for regression problems, the final prediction result is obtained by taking the average.
Random Forest can significantly reduce the risk of overfitting because it makes predictions through the ensemble of multiple decision trees. At the same time, since the construction of each decision tree is random, the random forest has better robustness and generalization ability.
Applications : Decision trees and random forests have a wide range of applications in various fields. They are often used in data mining, image recognition, natural language processing and other tasks. Decision tree can be used as a simple and efficient basic model, while random forest is one of the important methods to improve prediction performance and robustness. In practical applications, appropriate algorithms can be selected according to the characteristics of the problem and data to solve specific problems.
4.4. Logistic regression
Logistic Regression is a linear model commonly used to solve classification problems. Although it has "regression" in its name, it is actually a classification algorithm. Logistic regression is widely used in many practical applications, especially in binary classification problems.
Algorithm principle : The basic idea of logistic regression is to use a logistic function (also known as a sigmoid function) to map linearly combined features to probability values between 0 and 1. For binary classification problems, the model of logistic regression can be expressed as:
P(y=1∣x)=1/(1+e^−z)
P ( y =0∣ x )=1− P ( y =1∣ x )
Among them, P ( y =1∣ x ) represents the probability that the sample belongs to category 1 under the condition of given feature x; P ( y =0∣ x ) represents the probability of belonging to category 0; z is the linear combination of features x, Can be expressed as: z = w 0+ w 1 x 1+ w 2 x 2+…+ wmxm
Among them, w 0, w 1, w 2,…, wm are the parameters (weights) of the model, and x 1, x 2,…, xm are the features of the sample.
Model training : The training process of logistic regression is to find the optimal parameter values w 0, w 1, w 2,…, wm through maximum likelihood estimation , so that the prediction of the model on the training data is as close as possible to the real label. Training typically uses optimization algorithms such as gradient descent to minimize a loss function.
Decision boundary : Since the output of logistic regression is a probability value, we can set a threshold (usually 0.5) to decide the result of classification.
When P ( y =1∣ x ) is greater than the threshold, it is predicted to be category 1;
When P ( y =1∣ x ) is less than the threshold, class 0 is predicted. The decision boundary is obtained when the probability P ( y =1∣ x ) is equal to the threshold.
Pros and Cons : Logistic regression has the advantages of being simple, fast, and performs well on linearly separable problems. It is suitable for both small-scale data and high-dimensional data, and is easy to interpret and visualize. In addition, logistic regression can avoid overfitting through regularization methods.
However, the disadvantage of logistic regression is that it can only solve binary classification problems, and it needs to be extended for multi-classification problems. At the same time, it performs poorly in dealing with nonlinear problems, which may require feature engineering or use of more complex models.
Logistic regression is often used as a benchmark model for classification problems, especially when there is a linear relationship between features, it can be an effective classifier. In practice, logistic regression is often used in conjunction with other algorithms, or as part of more complex models.
4.5. Neural network
Neural Networks (Neural Networks) is a complex nonlinear model that simulates the way neurons in the human brain work, and it is the basis of deep learning. A neural network is a hierarchical structure composed of multiple neurons (also known as nodes or units), each neuron is connected to neurons in the previous and subsequent layers, processes the input through weights and activation functions, and generates output.
Structure of a Neural Network : A neural network usually consists of three basic layers:
- Input Layer: Receives raw data as input features, and each input feature corresponds to a neuron in the input layer.
- Hidden Layer: Between the input layer and the output layer, there may be multiple hidden layers. Each hidden layer consists of multiple neurons, and each neuron is connected to the neurons in the previous and subsequent layers.
- Output Layer: Output the final prediction result of the neural network, which can be classification labels, continuous values, etc. according to the task type.
How neurons work : Each neuron receives an input signal from the previous layer, performs calculations through weights and activation functions, and then passes the result to the next layer. How a neuron works involves two main steps:
- Linear combination: The neuron multiplies the input signal with the corresponding weight and sums these products to form a linear combination.
- Activation function: The result of the linear combination is processed by an activation function to produce the output of the neuron. Activation functions usually introduce non-linear properties, allowing neural networks to capture non-linear patterns and complex relationships.
Model training : The training of the neural network is realized through the backpropagation algorithm. According to the error between the predicted result and the real label, the algorithm reversely adjusts the weights in the neural network to minimize the error. The goal of training is to find the optimal weights so that the neural network can produce accurate predictions on new data.
Deep Learning : When a neural network has multiple hidden layers, it is called a deep neural network, and deep learning refers to a class of machine learning methods that use deep neural networks to solve problems. Deep learning has made remarkable progress in the fields of computer vision, natural language processing, speech recognition, etc., and has demonstrated powerful capabilities in various complex tasks.
Although neural networks have powerful expressive capabilities, due to their complexity, large amounts of data and computational resources are required for training to avoid overfitting. With the improvement of hardware performance and the continuous development of deep learning technology, neural networks have been widely used in various fields.
4.6. Linear regression
Linear regression (Linear Regression) is a commonly used linear model for solving regression problems. Regression problems refer to the task of predicting continuous numerical output, while the goal of linear regression is to find the optimal linear relationship to fit the relationship between features and target values.
Algorithm principle : In linear regression, we assume that there is a linear relationship between features and target values.
The basic form of the model can be expressed as: y = w 0+ w 1 x 1+ w 2 x 2+…+ wmxm + ε
Among them, y represents the target value, x 1, x 2,…, xm are features, w 0, w 1, w 2,…, wm are the parameters (weights) of the model, and ε represents the error term.
The goal of linear regression is to minimize the error between the predicted value and the real target value by finding the optimal parameters w 0, w 1, w 2,…, wm .
This is usually achieved by minimizing a loss function, the most common loss function being Mean Squared Error (MSE): MSE=1/n∑ i =1-> n ( yi − y ^ i ) ^ 2 where , n is the number of samples, yi is the true target value of the i-th sample, and y ^ i is the target value predicted by the model.
Model training : The training process of linear regression is to find the optimal parameter value through the optimization algorithm to minimize the loss function. The most commonly used method is the gradient descent algorithm, which calculates the gradient of the loss function with respect to the parameters and updates the parameter values in the direction of the gradient.
Advantages and disadvantages : The advantage of linear regression is that it is simple, easy to understand and implement. It is suitable for linearly separable problems and performs well when the amount of data is large and the features are linearly related.
However, the disadvantage of linear regression is its limited ability to fit data to complex nonlinear relationships. Linear regression may not predict well if the true relationship in the data is non-linear. In this case, you can try polynomial regression or other nonlinear models to solve the problem.
Linear regression is widely used in fields such as economics, finance, and natural sciences, especially in forecasting and trend analysis. In practical applications, an appropriate regression model can be selected according to the needs of the problem and the characteristics of the data.
4.7 Ridge regression
Ridge Regression is a regularized linear model for solving linear regression problems. It adds an L2 regularization term to ordinary linear regression to deal with feature collinearity and help prevent overfitting.
Algorithm principle : In linear regression, we assume that there is a linear relationship between the target value and the features. The basic form of the model can be expressed as: y = w 0+ w 1 x 1+ w 2 x 2+…+ wmxm + ε where y represents the target value, x 1, x 2,…, xm are features, w 0, w 1, w 2,…, wm are the parameters (weights) of the model, and ε represents the error term.
Ridge regression introduces the L2 regularization term on the basis of ordinary linear regression, which is the coefficient of the sum of squares of all parameters multiplied by a regularization parameter α: Ridge Loss=MSE + α ∑ i = 1-> m(wi)^ 2 Among them, MSE is the mean square error loss function of ordinary linear regression. By introducing a regularization term, ridge regression encourages the model weights to be as close to zero as possible, thereby reducing the collinearity between features, improving the generalization ability of the model, and helping to avoid overfitting problems.
Model training : The training process of ridge regression is to find the optimal parameters w 0, w 1, w 2,…, wm and regularization parameter α by minimizing the ridge loss function . This is usually achieved using optimization algorithms such as gradient descent.
Advantages and disadvantages : The advantage of ridge regression is that it can deal with the problem of feature collinearity, improve the stability and generalization ability of the model, and reduce the risk of overfitting. It is suitable for data sets with many features and strong correlation between features.
However, the disadvantage of ridge regression is that it relies on the choice of the regularization parameter α , which requires techniques such as cross-validation to determine the optimal value of α. Also, when the correlation between features is low, the effect of regularization may be small, and the effect of ridge regression and ordinary linear regression may be similar at this time.
Ridge regression is widely used in data analysis, financial modeling, signal processing and other fields, and is often used in practical applications to deal with high-dimensional data and collinearity problems.
4.8、K-means
Clustering K-means (K-Means Clustering) is a common unsupervised learning algorithm for clustering data. Clustering is to divide the data into different groups (clusters), so that the data points in the same group are more similar, while the data points in different groups are more different.
Algorithm principle : The working principle of the K-means algorithm is very simple and intuitive. Its steps are as follows:
- Initialization: randomly select k data points as the initial cluster centers (centroids).
- Assignment: Assign all data points to the cluster to which their nearest cluster center belongs.
- Update: Based on the currently assigned data points, new cluster centers are computed for each cluster.
- Repeat: Repeat steps 2 and 3 until the cluster centers no longer change or the specified number of iterations is reached.
Finally, the K-means algorithm divides the data points into k clusters, so that each data point belongs to the cluster to which the nearest cluster center belongs.
Select the value of K : In the K-means algorithm, the number k of clusters needs to be specified in advance. Choosing an appropriate value of k is often a challenging problem. A commonly used method is to select the optimal k value through evaluation indicators such as silhouette coefficient and sum of squared errors (SSE).
Pros and Cons : The K-means algorithm has the advantages of being simple, easy to implement and efficient. It is suitable for large-scale datasets and high-dimensional data. Clustering results are sensitive to the distribution of data and the selection of cluster centers.
However, the K-means algorithm has some disadvantages. First, it makes simple assumptions about the shape, size, and density of clusters and may not be suitable for complex data. Secondly, the K-means algorithm is sensitive to the selection of the initial cluster center, and different initial values may lead to different clustering results. Also, the K-means algorithm is not suitable for dealing with noisy data and outliers.
K-means algorithm is widely used in data mining, image processing, text clustering and other fields. In practical applications, you can choose an appropriate k value according to the characteristics of the data and the clustering goal, or use other more complex clustering algorithms to solve the problem.
5. Machine learning development process
The machine learning development pipeline is an iterative process that typically includes the following major steps:
- Problem definition :
-
- Identify the goals and problem type (classification, regression, clustering, etc.) of your machine learning project.
- Collect and understand the data set and define the business problem to be solved.
- Data preparation :
-
- Gather data: Get data from various sources, which can be databases, files, APIs, etc.
- Data cleaning: handle missing values, outliers and duplicate values to ensure data quality.
- Feature Engineering: Selecting, extracting, and transforming features to make them suitable for input to machine learning algorithms.
- Data splitting :
-
- Divide the dataset into training and testing sets for model training and evaluation.
- Choose a model :
-
- Choose the appropriate machine learning model based on the problem type and data characteristics.
- Different algorithms can be tried, compared and evaluated.
- Model training :
-
- Use the training set to train the selected model and adjust the parameters of the model.
- Optimization algorithms such as gradient descent are usually used to minimize the loss function.
- Model evaluation :
-
- Use the test set to evaluate the trained model, and calculate the indicators (accuracy, precision, recall, etc.) to measure the performance of the model.
- Verify that the model meets the expected accuracy and generalization capabilities.
- Model tuning :
-
- According to the evaluation results, the model is tuned, which may require adjustment of algorithms, feature engineering, or hyperparameters.
- Model deployment :
-
- Deploy the trained model to the production environment so that it can make predictions on new data.
- Monitoring and Maintenance :
-
- Monitor how your model performs in production to ensure its performance is stable.
- Regularly update the model to accommodate new data and business needs.
- Continuous Improvement :
-
- Continuously improve models and processes based on user feedback and business needs to optimize system performance and effects.
The machine learning development process is an iterative process that requires continuous optimization and improvement to adapt to changing data and business needs. At the same time, paying attention to key steps such as data quality, feature engineering, and model selection is very important for building an efficient and accurate machine learning system.
6. Learning framework
| name |
features |
| Scikit-learn |
A widely used Python machine learning library that provides a wealth of algorithms and tools. |
| TensorFlow |
A deep learning framework developed by Google that supports a variety of deep learning models. |
| PyTorch |
A deep learning framework developed by Facebook for flexibility and ease of use. |
| Hard |
High-level deep learning API that runs on backends like TensorFlow, PyTorch, etc. |
| XGBoost |
Excellent gradient boosting framework for classification and regression problems, handling large-scale datasets. |
| LightGBM |
An efficient gradient boosting framework developed by Microsoft, with fast training speed and low memory usage. |
| Pandas |
A powerful data analysis library that provides flexible data structures and processing tools for data preprocessing. |
| NLTK |
Python natural language processing toolkit for text and linguistic data processing. |
| OpenCV |
A popular computer vision library that provides image and video processing functions suitable for computer vision tasks. |
| Fast |
An advanced deep learning library based on PyTorch that simplifies deep learning tasks and is suitable for education and prototyping. |
| Theano |
A numerical computing library that supports the definition and optimization of deep learning models. |
| Caffe2 |
A deep learning framework developed by Facebook for deployment and mobile devices. |
| chain |
A Python-based deep learning framework that supports dynamic graph flexibility and easy expansion. |