python 机器学习基础教程——第一章，引言

た入场券 2021-10-30 04:18 327阅读 0赞

https://www.cnblogs.com/HolyShine/p/10819831.html

# from sklearn.datasets import load_iris
    import numpy as np #科学计算基础包
    from scipy import sparse
    import matplotlib.pyplot as plt
    import pandas as pd
    from IPython.display import  display
    import sys
    import matplotlib
    import sklearn
    from sklearn.datasets import load_iris
    from sklearn.model_selection import train_test_split
    from sklearn.neighbors import KNeighborsClassifier
    
    
    
    
    # x=np.array([[1,2,3],[4,5,6]])
    # print("x:\n{}".format(x))
    # eye=np.eye(4)
    # print("NumPy array:\n{}".format(eye))
    
    
    # x=np.linspace(-10,10,100)#在 -10和 10 之间生成一个数列，共100个数
    # #用正弦函数创建第二个数组
    # y=np.sin(x)
    # plt.plot(x,y,marker="x")#no display,why?
    
    #pandas
    # data={'Name':["John","Anna","Peter","Linda"],
    #       'Location':["New York","Paris","Berlin","London"],
    #       'Age':[24,13,53,33]
    #       }
    # data_pandas = pd.DataFrame(data)
    # display(data_pandas)
    #
    # display(data_pandas[data_pandas.Age>30])
    
    # print('Python Version:{}'.format(sys.version))
    # print('Pandas Version:{}'.format(pd.__version__))
    # print('matplotlib Version:{}'.format(matplotlib.__version__))
    # print('matplotlib Version:{}'.format(matplotlib.__version__))
    # print('scikit-learn Version:{}'.format(sklearn.__version__))
    
    
    iris_dataset=load_iris()
    # print("Keys of iris_dataset:\n{}".format(iris_dataset.keys()))
    
    X_train,X_test, y_train, y_test=train_test_split(
        iris_dataset['data'], iris_dataset['target'], random_state=0
    )
    # print("X_train sharpe:{}".format(X_train.shape))
    # print("y_train shape:{}".format(y_train.shape))
    #
    #
    # iris_dtaframe=pd.DataFrame(X_train, columns=iris_dataset.feature_names)
    # grr=pd.scatter_matrix(iris_dtaframe, c=y_train, figsize=(15,15), marker='O',hist_kwds={'bins':20}, s=60, alpha=.8, cmap=mglearn.cm3)
    
    #1.7.4 构建第一个模型：K邻近算法
    knn=KNeighborsClassifier(n_neighbors=1)
    knn.fit(X_train, y_train)
    #out
    KNeighborsClassifier(algorithm='auto',leaf_size=30,metric='minkowski',metric_params=None, n_jobs=1, n_neighbors=1, p=2, weights='uniform')
    
    X_new=np.array([[5,2.9,1,0.2]])
    print("X_new.shape:{}".format(X_new.shape))
    prediction=knn.predict(X_new)
    print("Prediction:{}".format(prediction))
    print("Predicted target name:{}".format(iris_dataset['target_names'][prediction]))
    
    y_pred=knn.predict(X_test)
    print("Test set predictions:\n{}".format(y_pred))
    print("Test set score:{:.2f}".format(np.mean(y_pred == y_test)))
    print("Test set score:{:.2f}".format(knn.score(X_test, y_test)))

转载于:https://www.cnblogs.com/quietwalk/p/10990743.html