训练模型

现在，是时候使用我们的数据集训练一些预测模型了。Scikit-learn提供了广泛的机器学习算法，它们具有统一/一致的接口，用于拟合，预测准确度等。

下面给出的示例使用KNN（K最近邻居）分类器。

注意：我们不会详细介绍算法的工作原理，因为我们只想了解它的实现。

现在，请考虑以下示例：

# load the iris dataset as an example

from sklearn.datasets import load_iris

iris = load_iris()

# store the feature matrix (X) and response vector (y)

X = iris.data

y = iris.target

# splitting X and y into training and testing sets

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=1)

# training the model on training set

from sklearn.neighbors import KNeighborsClassifier

knn = KNeighborsClassifier(n_neighbors=3)

knn.fit(X_train, y_train)

# making predictions on the testing set

y_pred = knn.predict(X_test)

# comparing actual response values (y_test) with predicted response values (y_pred)

from sklearn import metrics

print("kNN model accuracy:", metrics.accuracy_score(y_test, y_pred))

# making prediction for out of sample data

sample = [[3, 5, 4, 2], [2, 3, 5, 4]]

preds = knn.predict(sample)

pred_species = [iris.target_names[p] for p in preds]

print("Predictions:", pred_species)

# saving the model

from sklearn.externals import joblib

joblib.dump(knn, 'iris_knn.pkl')