Question

我正在处理一个看起来像这样的数据集：

在所附的屏幕截图中，您可以看到我的数据集包含16行和12个元组，但实际上它包含521行和12个元组。

第一列是：“蜜月早点开始”
第二列：“口服避孕药”
第三栏：“饮食维护”
第4栏：“受乳腺癌影响”
第5栏：“受宫颈癌影响吗？”
第6栏：“家庭癌症史？”
第7栏：“教育？”
第8列：“丈夫的年龄”
第9栏：“更年期结束年龄？”
第十栏：“食物中含有高脂肪？”
第11栏：“堕胎？”
第12栏：“是否患有卵巢癌？”

此处所有列均包含分类变量。因此，我通过使用LabelEncoder和OneHotEncoder对数据集进行了预处理，为了避免虚拟变量陷阱，我删除了虚拟变量的第一列，该列创建了两列以上。

然后我将数据集分为2部分，其中test_size = 0.25和random_state = 18，然后将X_train和y_train拟合到MultinomialNB（）并获得了准确度分数0.7938931297709924。

然后，我构建了几条类似的学习曲线和这个但最重要的是，我的模型为R平方赋值：0.557和Adj。 R平方：0.543，我认为这不好。

这是我的困惑矩阵
我希望r平方值和adj r平方值都在1左右，但是我不了解如何有效地做到这一点，因为我是这个领域的新手，并且之前从未使用过包含所有分类变量且没有值的任何数据集，如果您发现模型中有任何错误，请帮助我使用朴素贝叶斯算法使我的模型更好，请告诉我并提供帮助，并通过提供资源和教程+代码示例来构造数据可视化图，以帮助我我的模型。这是该项目的代码：

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd 

#Importing the dataset
dataset = pd.read_csv('RiskFactor.csv')
X =  dataset.iloc[:, :-1].values
y = dataset.iloc[:, 11].values
#dummy_x = dataset.iloc[:, [0,6,7,8]].values

from sklearn.preprocessing import LabelEncoder,OneHotEncoder

label_x = LabelEncoder()
X[:,0] = label_x.fit_transform(X[:,0] ) #Menarche start early

label_x = LabelEncoder()
X[:,1] = label_x.fit_transform(X[:,1] )

label_x = LabelEncoder()
X[:,2] = label_x.fit_transform(X[:,2] )

label_x = LabelEncoder()
X[:,3] = label_x.fit_transform(X[:,3] )

label_x = LabelEncoder()
X[:,4] = label_x.fit_transform(X[:,4] ) 
label_x = LabelEncoder()
X[:,5] = label_x.fit_transform(X[:,5] )

label_x = LabelEncoder()
X[:,6] = label_x.fit_transform(X[:,6] ) #Education

label_x = LabelEncoder()
X[:,7] = label_x.fit_transform(X[:,7] ) #Age of Husband

label_x = LabelEncoder()
X[:,8] = label_x.fit_transform(X[:,8] ) #Menopause End age?

label_x = LabelEncoder()
X[:,9] = label_x.fit_transform(X[:,9] )


label_x = LabelEncoder()
X[:,10] = label_x.fit_transform(X[:,10] )

onehotencoder = OneHotEncoder(categorical_features = "all")
X = onehotencoder.fit_transform(X).toarray()


#avoiding dummy variable trap by removing extra columns 

X = X[: ,[1,2,3,4,5,6,7,8,9,10,11,12,14,15,17,18,20,21,22,23,24,25,26]]


# Encoding the Dependent Variable

labelencoder_y = LabelEncoder()
y = labelencoder_y.fit_transform(y)

# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size =0.25,
random_state = 18)

from sklearn.naive_bayes import GaussianNB,BernoulliNB,MultinomialNB

classifier = MultinomialNB()
classifier.fit(X_train, y_train)

print(classifier)

y_expect = y_test



#predicting the test set result

y_pred = classifier.predict(X_test)

#Making the Confusion Matrix

from sklearn.metrics import confusion_matrix,accuracy_score

cm = confusion_matrix (y_test, y_pred)


print(accuracy_score(y_expect,y_pred))


# finding P value from statsmodels

import statsmodels.formula.api as sm

regressor_OLS = sm.OLS(endog=y,exog = X).fit()

regressor_OLS.summary()

from sklearn.model_selection import learning_curve
from sklearn.model_selection import ShuffleSplit


def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
                    n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):
"""
Generate a simple plot of the test and training learning curve.

Parameters
----------
estimator : object type that implements the "fit" and "predict" methods
    An object of that type which is cloned for each validation.

title : string
    Title for the chart.

X : array-like, shape (n_samples, n_features)
    Training vector, where n_samples is the number of samples and
    n_features is the number of features.

y : array-like, shape (n_samples) or (n_samples, n_features), optional
    Target relative to X for classification or regression;
    None for unsupervised learning.

ylim : tuple, shape (ymin, ymax), optional
    Defines minimum and maximum yvalues plotted.

cv : int, cross-validation generator or an iterable, optional
    Determines the cross-validation splitting strategy.
    Possible inputs for cv are:
      - None, to use the default 3-fold cross-validation,
      - integer, to specify the number of folds.
      - An object to be used as a cross-validation generator.
      - An iterable yielding train/test splits.

    For integer/None inputs, if ``y`` is binary or multiclass,
    :param train_sizes:
    :class:`StratifiedKFold` used. If the estimator is not a classifier
    or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.

    Refer :ref:`User Guide <cross_validation>` for the various
    cross-validators that can be used here.

 n_jobs : integer, optional
    Number of jobs to run in parallel (default 1).
"""
plt.figure()
plt.title(title)
if ylim is not None:
    plt.ylim(*ylim)
plt.xlabel("Training examples")
plt.ylabel("Score")
train_sizes, train_scores, test_scores = learning_curve(
    estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
plt.grid()

plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
                 train_scores_mean + train_scores_std, alpha=0.1,
                 color="r")
plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
                 test_scores_mean + test_scores_std, alpha=0.1, color="g")
plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
         label="Training score")
plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
         label="Cross-validation score")

plt.legend(loc="best")
return plt



estimator = MultinomialNB()


title = "Learning Curves (Naive Bayes classifier ALGORITHM)"
# Cross validation with 100 iterations to get smoother mean test and train
# score curves, each time with 20% data randomly selected as a validation    

#set.
cv = ShuffleSplit(n_splits=100, test_size=0.25, random_state=17)

#cv = None
plot_learning_curve(estimator, title, X, y, ylim=(0.7, 1.01), cv=cv,    
n_jobs=1)

plt.show()

Answer 1

I've solved this problem by using PCA ,here is the code :   


# -*- coding: utf-8 -*-
"""
Created on Tue Jul 31 22:38:32 2018

@author: MOBASSIR
"""


# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd




#Importing the dataset
dataset = pd.read_csv('ovarian.csv')
X =  dataset.iloc[:, :-1].values
y = dataset.iloc[:, 11].values
#dummy_x = dataset.iloc[:, [0,6,7,8]].values

from sklearn.preprocessing import LabelEncoder,OneHotEncoder

label_x1 = LabelEncoder()
X[:,0] = label_x1.fit_transform(X[:,0] ) #Menarche start early



label_x2 = LabelEncoder()
X[:,1] = label_x2.fit_transform(X[:,1] )

label_x3 = LabelEncoder()
X[:,2] = label_x3.fit_transform(X[:,2] )


label_x4 = LabelEncoder()
X[:,3] = label_x4.fit_transform(X[:,3] )


label_x5 = LabelEncoder()
X[:,4] = label_x5.fit_transform(X[:,4] )


label_x6 = LabelEncoder()
X[:,5] = label_x6.fit_transform(X[:,5] )


label_x7 = LabelEncoder()
X[:,6] = label_x7.fit_transform(X[:,6] ) #Education




label_x8 = LabelEncoder()
X[:,7] = label_x8.fit_transform(X[:,7] ) #Age of Husband




label_x9 = LabelEncoder()
X[:,8] = label_x9.fit_transform(X[:,8] ) #Menopause End age?




label_x10 = LabelEncoder()
X[:,9] = label_x10.fit_transform(X[:,9] )




label_x11 = LabelEncoder()
X[:,10] = label_x11.fit_transform(X[:,10] )




onehotencoder = OneHotEncoder(categorical_features = [0,6,7,8])
X = onehotencoder.fit_transform(X).toarray()


# Avoiding the Dummy Variable Trap

"""

idx_to_delete = [0, 13, 16, 19]
X = [i for i in range(X.shape[-1]) if i not in idx_to_delete]

X = X[:, 1:]


df = pd.DataFrame(X, dtype='float64')


df = pd.to_numeric(X)

"""


#avoiding dummy variable trap by removing extra columns

#X = X[: ,[1,2,3,4,5,6,7,8,9,10,11,12,14,15,17,18,20,21,22,23,24,25,26]]

"""
#4,8,10,12,18,21,22,23 for dropped columns
#5,9,11,13,19,22,23,24 for dropped columns
#1,4,5,6 == 2,5,6,7
X = X[: ,[9,11,23,24]]
"""

 #24,21,19,18,17,14,12,10,8,7,6 ,4 ,3 ,2,1 for undropped column
 #25,22,20,19,18,15,13,11,9,8,7 ,5 ,4 ,3,2
 #2,5,6,8,12,15
X = X[: ,[9,13,16,18,19]]


# Encoding the Dependent Variable

labelencoder_y = LabelEncoder()
y = labelencoder_y.fit_transform(y)
"""
onehotencoder = OneHotEncoder()
y= onehotencoder.fit_transform(y).toarray()
"""





# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)




# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)




# Applying PCA
from sklearn.decomposition import PCA
pca = PCA(n_components = 2)
X_train = pca.fit_transform(X_train)
X_test = pca.transform(X_test)
explained_variance = pca.explained_variance_ratio_






#Applying naive bayes classifier

from sklearn.naive_bayes import GaussianNB,BernoulliNB,MultinomialNB

classifier = BernoulliNB()
classifier.fit(X_train, y_train)

print(classifier)

y_expect = y_test



#predicting the test set result

y_pred = classifier.predict(X_test)

#Making the Confusion Matrix

from sklearn.metrics import confusion_matrix,accuracy_score

cm = confusion_matrix (y_test, y_pred)


print(accuracy_score(y_expect,y_pred))





# finding P value from statsmodels

import statsmodels.formula.api as sm

regressor_OLS = sm.OLS(endog=y,exog = X).fit()

regressor_OLS.summary()




from sklearn.cross_validation import cross_val_score

ck =  BernoulliNB()
scores = cross_val_score(ck,X,y,cv=10, scoring='accuracy')
print (scores)

print (scores.mean())




from sklearn.model_selection import learning_curve
from sklearn.model_selection import ShuffleSplit

def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):



    '''Generate a simple plot of the test and training learning curve.

   Parameters
   ----------
   estimator : object type that implements the "fit" and "predict" methods
       An object of that type which is cloned for each validation.

   title : string
       Title for the chart.

   X : array-like, shape (n_samples, n_features)
       Training vector, where n_samples is the number of samples and
       n_features is the number of features.

   y : array-like, shape (n_samples) or (n_samples, n_features), optional
       Target relative to X for classification or regression;
       None for unsupervised learning.

   ylim : tuple, shape (ymin, ymax), optional
       Defines minimum and maximum yvalues plotted.

   cv : int, cross-validation generator or an iterable, optional
       Determines the cross-validation splitting strategy.
       Possible inputs for cv are:
         - None, to use the default 3-fold cross-validation,
         - integer, to specify the number of folds.
         - An object to be used as a cross-validation generator.
         - An iterable yielding train/test splits.

       For integer/None inputs, if ``y`` is binary or multiclass,
       :param train_sizes:
       :class:`StratifiedKFold` used. If the estimator is not a classifier
       or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.

       Refer :ref:`User Guide <cross_validation>` for the various
       cross-validators that can be used here.

   n_jobs : integer, optional
       Number of jobs to run in parallel (default 1).'''


    plt.figure()
    plt.title(title)
    if ylim is not None:
        plt.ylim(*ylim)
    plt.xlabel("Training examples")
    plt.ylabel("Score")
    train_sizes, train_scores, test_scores = learning_curve(
        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
    train_scores_mean = np.mean(train_scores, axis=1)
    train_scores_std = np.std(train_scores, axis=1)
    test_scores_mean = np.mean(test_scores, axis=1)
    test_scores_std = np.std(test_scores, axis=1)
    plt.grid()

    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
                     train_scores_mean + train_scores_std, alpha=0.1,
                     color="r")
    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
                     test_scores_mean + test_scores_std, alpha=0.1, color="g")
    plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
             label="Training score")
    plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
             label="Cross-validation score")

    plt.legend(loc="best")
    return plt



estimator = BernoulliNB()


title = "Learning Curves (Naive Bayes classifier ALGORITHM)"
# Cross validation with 100 iterations to get smoother mean test and train
# score curves, each time with 20% data randomly selected as a validation set.
cv = ShuffleSplit(n_splits=100, test_size=0.25, random_state=0)

plot_learning_curve(estimator, title, X, y, ylim=(0.7, 1.01), cv=cv, n_jobs=1)

plt.show()

#End of Bayes theorem


plt.rcParams['font.size'] = 14

plt.hist(y_pred, bins = 8)

plt.xlim(0, 1)

plt.title('Predicted probabilities')
plt.xlabel('Affected by ovarian cancer?(predicted)')
plt.ylabel('frequency')




from sklearn.metrics import recall_score,precision_score

recall_score(y_test,y_pred,average='macro')

precision_score(y_test, y_pred, average='micro')








# Visualising the Training set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Naive Bayes (Training set)')
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.legend()
plt.show()

# Visualising the Test set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
             alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                c = ListedColormap(('red', 'green'))(i), label = j)
plt.title('Naive Bayes (Test set)')
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.legend()
plt.show()

如何使用sklearn提高MultinomialNB（）的准确性得分，并使用matplotlib在图表中可视化结果？

1 个答案: