Question

这是我的代码我构建6个模型，我正在准确，我如何动态选择哪个精度更高，我想只执行最高精度的模型。

"prepare configuration for cross validation test harness"

seed = 7

"prepare models"

models = []
models.append(('LR', LogisticRegression()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('RF',RandomForestClassifier()))

#models.append(('SVM', SVC()))

"evaluate each model in turn"

results = []
names = []
scoring = 'accuracy'
for name, model in models:
    kfold = model_selection.KFold(n_splits=10, random_state=seed)
    cv_results = model_selection.cross_val_score(model, orginal_telecom_80p_test[features], orginal_telecom_80p_test["Churn"], cv=kfold, scoring=scoring)
    results.append(cv_results)
    names.append(name)
    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
    print(msg)

这是我的准确性

LR: 0.787555 (0.039036)
LDA: 0.780460 (0.039821)
KNN: 0.759916 (0.030417)
CART: 0.706669 (0.035827)
NB: 0.731637 (0.050813)
RF: 0.752054 (0.048660)

Answer 1

正如我在评论中提到的，大部分代码段完全无关紧要，应该用简化的 runnable 示例替换。

现在，如果你质疑的是＆＃34;我有那些我可以获得分数的对象＆＃39;我想选择分数较高的那个＆＃34;，它非常简单：将分数与对象一起存储，根据分数对其进行排序并保持得分最高的分数：

import random

def get_score(model):
    # dumbed down example 
    return random.randint(1, 10)


class Model1(object):
    pass

class Model2(object):
    pass

class Model3(object):
    pass

models = [Model1, Model2, Model3]

# build a list of (score, model) tuples
scores = [(get_score(model), model) for model in models]

# sort it on score
scores.sort(key=item[0])

# get the model with the best score, which is the
# the second element of the last item
best = scores[-1][1]

现在请自己和世界一个忙：学习清除问题所有相关信息和仅相关信息

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如何使用python动态选择最佳模型

1 个答案: