In layman’s terms, a machine learning model is a mathematical function that maps input data to a predicted output. More specifically, a machine learning model is a mathematical function that adjusts model parameters by learning from training data to minimize the error between the predicted output and the true label.

There are many models in machine learning, such as logistic regression model, decision tree model, support vector machine model, etc. Each model has its application data types and question types. At the same time, there are many commonalities between different models, or there is a hidden path for model evolution.

Take the connectionist perceptron as an example. By increasing the number of hidden layers of the perceptron, we can transform it into a deep neural network. If a kernel function is added to the perceptron, it can be converted into an SVM. This process can intuitively demonstrate the intrinsic connections between different models, as well as the possible transformations between models. According to the similarities, I roughly (not rigorously) divided the models into the following 6 categories to facilitate the discovery of basic commonalities and analyze them in depth one by one!

1. Neural network (connectionist) models:

The connectionist model is a computing model that simulates the structure and function of the human brain neural network. . Its basic unit is a neuron. Each neuron receives input from other neurons and changes the influence of the input on the neuron by adjusting the weight. The neural network is a black box. Through the action of multiple nonlinear hidden layers, it can achieve close to the effect.

Representative models include DNN, SVM, Transformer, and LSTM. In some cases, the last layer of the deep neural network can be regarded as a logic Regression model used to classify input data. The support vector machine can also be regarded as a special type of neural network. There are only two layers in it: the input layer and the output layer. SVM additionally implements complex nonlinear transformation through kernel functions to achieve results similar to deep neural networks. Effect. The following is an analysis of the principle of the classic DNN model:

Deep neural network (DNN) is composed of multiple layers of neurons and passes the input data through the forward propagation process. To each layer of neurons, the output is obtained through layer-by-layer calculation. Each layer of neurons receives the output of the previous layer's neurons as input and outputs it to the next layer's neurons. The training process of DNN is implemented through the back propagation algorithm. During the training process, the error between the output layer and the real label is calculated, the error is back-propagated to each layer of neurons, and the weights and bias terms of the neurons are updated according to the gradient descent algorithm. By repeatedly iterating this process, the network parameters are continuously optimized, and the prediction error of the network is ultimately minimized.

The advantage of deep neural network (DNN) is its powerful feature learning ability. DNN can automatically learn the characteristics of data without manually designing features. Highly nonlinear and strong generalization ability. The disadvantage is that DNN requires a large number of parameters, which may lead to overfitting problems. At the same time, DNN requires a large amount of calculation and takes a long time to train. The following is a simple Python code example, using the Keras library to build a deep neural network model:

from keras.models import Sequentialfrom keras.layers import Densefrom keras.optimizers import Adamfrom keras.losses import BinaryCrossentropyimport numpy as np# 構(gòu)建模型model = Sequential()model.add(Dense(64, activatinotallow='relu', input_shape=(10,))) # 輸入層有10個(gè)特征model.add(Dense(64, activatinotallow='relu')) # 隱藏層有64個(gè)神經(jīng)元model.add(Dense(1, activatinotallow='sigmoid')) # 輸出層有1個(gè)神經(jīng)元，使用sigmoid激活函數(shù)進(jìn)行二分類任務(wù)# 編譯模型model.compile(optimizer=Adam(lr=0.001), loss=BinaryCrossentropy(), metrics=['accuracy'])# 生成模擬數(shù)據(jù)集x_train = np.random.rand(1000, 10) # 1000個(gè)樣本，每個(gè)樣本有10個(gè)特征y_train = np.random.randint(2, size=1000) # 1000個(gè)標(biāo)簽，二分類任務(wù)# 訓(xùn)練模型model.fit(x_train, y_train, epochs=10, batch_size=32) # 訓(xùn)練10個(gè)輪次，每次使用32個(gè)樣本進(jìn)行訓(xùn)練

Symbolism model is an intelligent simulation method based on logical reasoning. It believes that human beings are a physical symbol system and computers are also physical symbol systems. Therefore, the computer’s rule base and reasoning engine can be used to simulate human beings. Intelligent behavior is to use computer symbolic operations to simulate human cognitive processes (to put it bluntly, it is to store human logic into computers to achieve intelligent execution).

The representative models include expert systems, knowledge bases, and knowledge graphs. The principle is to encode information into a set of identifiable symbols, through explicit Rules for manipulating symbols to produce results. A simple example of an expert system is as follows:

# 定義規(guī)則庫(kù)rules = [{"name": "rule1", "condition": "sym1 == 'A' and sym2 == 'B'", "action": "result = 'C'"},{"name": "rule2", "condition": "sym1 == 'B' and sym2 == 'C'", "action": "result = 'D'"},{"name": "rule3", "condition": "sym1 == 'A' or sym2 == 'B'", "action": "result = 'E'"},]# 定義推理引擎def infer(rules, sym1, sym2):for rule in rules:if rule["condition"] == True:# 條件為真時(shí)執(zhí)行動(dòng)作return rule["action"]return None# 沒(méi)有滿足條件的規(guī)則時(shí)返回None# 測(cè)試專家系統(tǒng)print(infer(rules, 'A', 'B'))# 輸出: Cprint(infer(rules, 'B', 'C'))# 輸出: Dprint(infer(rules, 'A', 'C'))# 輸出: Eprint(infer(rules, 'B', 'B'))# 輸出: E

from sklearn.datasets import load_irisfrom sklearn.model_selection import train_test_splitfrom sklearn.tree import DecisionTreeClassifier, plot_tree# 加載數(shù)據(jù)集iris = load_iris()X = iris.datay = iris.target# 劃分訓(xùn)練集和測(cè)試集X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# 構(gòu)建決策樹模型clf = DecisionTreeClassifier(criterinotallow='gini')clf.fit(X_train, y_train)# 預(yù)測(cè)測(cè)試集結(jié)果y_pred = clf.predict(X_test)# 可視化決策樹plot_tree(clf)

from sklearn.datasets import load_irisfrom sklearn.model_selection import train_test_splitfrom sklearn.naive_bayes import GaussianNB# 加載數(shù)據(jù)集iris = load_iris()X = iris.datay = iris.target# 劃分訓(xùn)練集和測(cè)試集X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# 構(gòu)建樸素貝葉斯分類器模型clf = GaussianNB()clf.fit(X_train, y_train)# 預(yù)測(cè)測(cè)試集結(jié)果y_pred = clf.predict(X_test)

from sklearn.datasets import load_irisfrom sklearn.model_selection import train_test_splitfrom sklearn.neighbors import KNeighborsClassifier# 加載數(shù)據(jù)集iris = load_iris()X = iris.datay = iris.target# 劃分訓(xùn)練集和測(cè)試集X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# 構(gòu)建KNN分類器模型knn = KNeighborsClassifier(n_neighbors=3)knn.fit(X_train, y_train)# 預(yù)測(cè)測(cè)試集結(jié)果y_pred = knn.predict(X_test)

from sklearn.ensemble import RandomForestClassifierfrom sklearn.datasets import load_irisfrom sklearn.model_selection import train_test_split# 加載數(shù)據(jù)集iris = load_iris()X = iris.datay = iris.target# 劃分訓(xùn)練集和測(cè)試集X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# 構(gòu)建隨機(jī)森林分類器模型clf = RandomForestClassifier(n_estimators=100, random_state=42)clf.fit(X_train, y_train)# 預(yù)測(cè)測(cè)試集結(jié)果y_pred = clf.predict(X_test)