下面是一个示例代码，用于从txt文件中读取数据，根据特征值应用三种人工智能模型算法，并输出结果和显示相关图片： ```python import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import CountVectorizer from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC from sklearn.tree import DecisionTreeClassifier # 读取数据文件 data = pd.read_csv('data.txt', delimiter='\t', header=None) # 分割特征值和标签 X = data.iloc[:, :-1] y = data.iloc[:, -1] # 将文本特征转换为向量表示 vectorizer = CountVectorizer() X_vectorized = vectorizer.fit_transform(X) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X_vectorized, y, test_size=0.2, random_state=42) # 模型1：逻辑回归 model_lr = LogisticRegression() model_lr.fit(X_train, y_train) score_lr = model_lr.score(X_test, y_test) # 模型2：支持向量机 model_svm = SVC() model_svm.fit(X_train, y_train) score_svm = model_svm.score(X_test, y_test) # 模型3：决策树 model_dt = DecisionTreeClassifier() model_dt.fit(X_train, y_train) score_dt = model_dt.score(X_test, y_test) # 比较三种模型得分 scores = {'Logistic Regression': score_lr, 'SVM': score_svm, 'Decision Tree': score_dt} best_model = max(scores, key=scores.get) # 输出结果 print('模型得分:') for model, score in scores.items(): print(f"{model}: {score}") print(f"最优模型: {best_model}") # 显示特征向量的词云图 word_count = pd.Series(np.array(X_vectorized.sum(axis=0)).flatten(), index=vectorizer.get_feature_names()) word_count.sort_values(ascending=False)[:50].plot(kind='bar', figsize=(10, 6)) plt.xlabel('词汇') plt.ylabel('词频') plt.title('Top 50 词汇') plt.show() ``` 上述示例代码假设数据文件为`data.txt`，特征值在每一行的前列，标签在每行的最后一列，特征值以制表符分隔。您需要根据具体的数据文件格式适当修改代码。根据数据集的大小和特征的复杂性，选择合适的机器学习模型和相关参数进行调整。 [2024-05-31 15:20:58 | AI写代码神器 | 1324点数解答]