df_train.shape

准备训练集数据,测试集数据:
1. df_train_target：目标,也就是count字段。
2. df_train_data：用于产出特征的数据

df_train_target = df_train['count'].values 
print(df_train_target.shape) 
df_train_data = df_train.drop(['count'],axis =1).values
print(df_train_data.shape) 

算法
咱们依旧会使用交叉验证的方式（交叉验证集约占全部数据的20%）来看看模型的效果,
我们会试 支持向量回归/Suport Vector Regression, 岭回归/Ridge Regression 和
随机森林回归/Random Forest Regressor。每个模型会跑3趟看平均的结果。

from sklearn import linear_model
from sklearn import cross_validation
from sklearn import svm
from sklearn.ensemble import RandomForestRegressor
from sklearn.learning_curve import learning_curve
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import explained_variance_score

# 切分一下数据（训练集和测试集）
cv = cross_validation.ShuffleSplit(len(df_train_data), n_iter=3, test_size=0.2,
    random_state=0)

# 各种模型来一圈

print("岭回归")    
for train, test in cv:    
    svc = linear_model.Ridge().fit(df_train_data[train], df_train_target[train])
    print("train score: {0:.3f}, test score: {1:.3f}\n".format(
        svc.score(df_train_data[train], df_train_target[train]), svc.score(df_train_data[test], df_train_target[test])))
    
print("支持向量回归/SVR(kernel='rbf',C=10,gamma=.001)")
for train, test in cv:
    
    svc = svm.SVR(kernel ='rbf', C = 10, gamma = .001).fit(df_train_data[train], df_train_target[train])
    print("train score: {0:.3f}, test score: {1:.3f}\n".format(
        svc.score(df_train_data[train], df_train_target[train]), svc.score(df_train_data[test], df_train_target[test])))
    
print("随机森林回归/Random Forest(n_estimators = 100)")    
for train, test in cv:    
    svc = RandomForestRegressor(n_estimators = 100).fit(df_train_data[train], df_train_target[train])
    print("train score: {0:.3f}, test score: {1:.3f}\n".format(
        svc.score(df_train_data[train], df_train_target[train]), svc.score(df_train_data[test], df_train_target[test])))

随机森林回归获得了最佳结果
不过,参数设置得是不是最好的,这个我们可以用GridSearch来帮助测试,找最好的参数

X = df_train_data
y = df_train_target

X_train, X_test, y_train, y_test = cross_validation.train_test_split(
    X, y, test_size=0.2, random_state=0)

tuned_parameters = [{'n_estimators':[10,100,500,550]}]   
    
scores = ['r2']

for score in scores:
    
    print(score)
    
    clf = GridSearchCV(RandomForestRegressor(), tuned_parameters, cv=5, scoring=score)
    clf.fit(X_train, y_train)

    print("最佳参数找到了：")
    print("")
    #best_estimator_ returns the best estimator chosen by the search
    print(clf.best_estimator_)
    print("")
    print("得分分别是:")
    print("")
    #grid_scores_的返回值:
    #    * a dict of parameter settings
    #    * the mean score over the cross-validation folds 
    #    * the list of scores for each fold
    for params, mean_score, scores in clf.grid_scores_:
        print("%0.3f (+/-%0.03f) for %r"
              % (mean_score, scores.std() / 2, params))
    print("")

Grid Search帮挑参数还是蛮方便的, 而且要看看模型状态是不是,过拟合or欠拟合
我们发现n_estimators=500,550时,拟合得最好。