[Feature] decision surface, regression and KNN

machine_learning/decision_tree/decision_surface_iris.py

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+#
+from sklearn.datasets import load_iris
+iris = load_iris()
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from sklearn.datasets import load_iris
+from sklearn.inspection import DecisionBoundaryDisplay
+from sklearn.tree import DecisionTreeClassifier
+
+# Parameters
+n_classes = 3
+plot_colors = "ryb"
+plot_step = 0.02
+
+
+for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]]):
+    # We only take the two corresponding features
+    X = iris.data[:, pair]
+    y = iris.target
+
+    # Train
+    clf = DecisionTreeClassifier().fit(X, y)
+
+    # Plot the decision boundary
+    ax = plt.subplot(2, 3, pairidx + 1)
+    plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)
+    DecisionBoundaryDisplay.from_estimator(
+        clf,
+        X,
+        cmap=plt.cm.RdYlBu,
+        response_method="predict",
+        ax=ax,
+        xlabel=iris.feature_names[pair[0]],
+        ylabel=iris.feature_names[pair[1]],
+    )
+
+    # Plot the training points
+    for i, color in zip(range(n_classes), plot_colors):
+        idx = np.where(y == i)
+        plt.scatter(
+            X[idx, 0],
+            X[idx, 1],
+            c=color,
+            label=iris.target_names[i],
+            cmap=plt.cm.RdYlBu,
+            edgecolor="black",
+            s=15,
+        )
+
+plt.suptitle("Decision surface of decision trees trained on pairs of features")
+plt.legend(loc="lower right", borderpad=0, handletextpad=0)
+_ = plt.axis("tight")
+
+
+#
+from sklearn.tree import plot_tree
+
+plt.figure()
+clf = DecisionTreeClassifier().fit(iris.data, iris.target)
+plot_tree(clf, filled=True)
+plt.title("Decision tree trained on all the iris features")
+plt.show()

machine_learning/decision_tree/regression_train.py

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+# 决策树
+# Import the necessary modules and libraries
+import matplotlib.pyplot as plt
+import numpy as np
+
+from sklearn.tree import DecisionTreeRegressor
+
+# Create a random dataset
+rng = np.random.RandomState(1)
+X = np.sort(5 * rng.rand(80, 1), axis=0)
+y = np.sin(X).ravel()
+y[::5] += 3 * (0.5 - rng.rand(16))
+
+# Fit regression model
+regr_1 = DecisionTreeRegressor(max_depth=2)
+regr_2 = DecisionTreeRegressor(max_depth=5)
+regr_1.fit(X, y)
+regr_2.fit(X, y)
+
+# Predict
+X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]
+y_1 = regr_1.predict(X_test)
+y_2 = regr_2.predict(X_test)
+
+# Plot the results
+plt.figure()
+plt.scatter(X, y, s=20, edgecolor="black", c="darkorange", label="data")
+plt.plot(X_test, y_1, color="cornflowerblue", label="max_depth=2", linewidth=2)
+plt.plot(X_test, y_2, color="yellowgreen", label="max_depth=5", linewidth=2)
+plt.xlabel("data")
+plt.ylabel("target")
+plt.title("Decision Tree Regression")
+plt.legend()
+plt.show() 

machine_learning/nearest_neighbors/KNN_iris.py

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+#鸢尾KNN算法分类   
+#https://blog.csdn.net/hqllqh/article/details/108914072
+
+import numpy as np
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.neighbors import KNeighborsClassifier
+
+iris_data = load_iris()
+# 该函数返回一个Bunch对象，它直接继承自Dict类，与字典类似，由键值对组成。
+# 可以使用bunch.keys(),bunch.values(),bunch.items()等方法。
+print(type(iris_data)) 
+# data里面是花萼长度、花萼宽度、花瓣长度、花瓣宽度的测量数据，格式为 NumPy数组
+print(iris_data['data'])    # 花的样本数据
+print("花的样本数量：{}".format(iris_data['data'].shape))   
+print("花的前5个样本数据：{}".format(iris_data['data'][:5])) 
+
+# 0 代表 setosa， 1 代表 versicolor，2 代表 virginica
+print(iris_data['target'])  # 类别
+print(iris_data['target_names'])   # 花的品种
+
+# 构造训练数据和测试数据
+X_train,X_test,y_train,y_test = train_test_split(\
+    iris_data['data'],iris_data['target'],random_state=0)
+print("训练样本数据的大小：{}".format(X_train.shape))
+print("训练样本标签的大小：{}".format(y_train.shape))
+print("测试样本数据的大小：{}".format(X_test.shape))
+print("测试样本标签的大小：{}".format(y_test.shape))
+
+# 构造KNN模型
+knn = KNeighborsClassifier(n_neighbors=1)
+# knn = KNeighborsClassifier(n_neighbors=3)
+
+# 训练模型
+knn.fit(X_train,y_train)
+y_pred = knn.predict(X_test)
+
+# 评估模型
+print("模型精度：{:.2f}".format(np.mean(y_pred==y_test)))
+print("模型精度：{:.2f}".format(knn.score(X_test,y_test)))
+
+# 做出预测
+X_new = np.array([[1.1,5.9,1.4,2.2]])
+prediction = knn.predict(X_new)
+print("预测的目标类别是：{}".format(prediction))
+print("预测的目标类别花名是：{}".format(iris_data['target_names'][prediction]))