scikit在分类器上的准确性很低(朴素贝叶斯，DecissionTreeClassifier)

问题描述

我正在使用此数据集基于年龄的健康，并且文档中指出准确度应在 84％左右.不幸的是，我的程序的准确性为 25％

I am using this dataset Weath Based on age and the documentation states that the accuracy should be around 84%. Unfortunately, the accuracy of my program is at 25%

要处理数据，我执行了以下操作:

To process the data I did the following:

1. Loaded the .txt data file and converted it to a .csv
2. Removed data with missing values
3. Extracted the class values: <=50K >50 and convert it to 0 and 1 respectively
4. For each attribute and for each string value of that attribute I
   mapped it to an integer value. Example att1{'cs':0, 'cs2':1},
   att2{'usa':0, 'greece':1} ... and so on
5. Called naive bayes on the new integer data set

Python代码:

import load_csv as load #my functions to do [1..5] of the list
import numpy as np

my_data = np.genfromtxt('out.csv', dtype = dt, delimiter = ',', skip_header = 1)

data = np.array(load.remove_missing_values(my_data))                     #this funcion removes the missing data
features_train = np.array(load.remove_field_num(data, len(data[0]) - 1)) #this function extracts the data, e.g removes the class in the end of the data

label_train = np.array(load.create_labels(data))
features_train = np.array(load.convert_to_int(features_train))


my_data = np.genfromtxt('test.csv', dtype = dt, delimiter = ',', skip_header = 1)

data = np.array(load.remove_missing_values(my_data))
features_test = np.array(load.remove_field_num(data, len(data[0]) - 1))

label_test = np.array(load.create_labels(data))                          #extracts the labels from the .csv data file
features_test = np.array(load.convert_to_int(features_test))             #converts the strings to ints(each unique string of an attribute is assigned a unique integer value

from sklearn import tree
from sklearn.naive_bayes import GaussianNB
from sklearn import tree
from sklearn.metrics import accuracy_score

clf = tree.DecisionTreeClassifier()
clf.fit(features_train, label_train)
predict = clf.predict(features_test)

score = accuracy_score(predict, label_test) #Low accuracy score

load_csv模块:

load_csv module:

import numpy as np

attributes = {  'Private':0, 'Self-emp-not-inc':1, 'Self-emp-inc':2, 'Federal-gov':3, 'Local-gov':4, 'State-gov':5, 'Without-pay':6, 'Never-worked':7,
            'Bachelors':0, 'Some-college':1, '11th':2, 'HS-grad':3, 'Prof-school':4, 'Assoc-acdm':5, 'Assoc-voc':6, '9th':7, '7th-8th':8, '12th':9, 'Masters':10, '1st-4th':11, '10th':12,                  'Doctorate':13, '5th-6th':14, 'Preschool':15,
            'Married-civ-spouse':0, 'Divorced':1, 'Never-married':2, 'Separated':3, 'Widowed':4, 'Married-spouse-absent':5, 'Married-AF-spouse':6,
            'Tech-support':0, 'Craft-repair':1, 'Other-service':2, 'Sales':3, 'Exec-managerial':4, 'Prof-specialty':5, 'Handlers-cleaners':6, 'Machine-op-inspct':7, 'Adm-clerical':8,
            'Farming-fishing':9, 'Transport-moving':10, 'Priv-house-serv':11, 'Protective-serv':12, 'Armed-Forces':13,
            'Wife':0, 'Own-child':1, 'Husband':2, 'Not-in-family':4, 'Other-relative':5, 'Unmarried':5,
            'White':0, 'Asian-Pac-Islander':1, 'Amer-Indian-Eskimo':2, 'Other':3, 'Black':4,
            'Female':0, 'Male':1,
            'United-States':0, 'Cambodia':1, 'England':2, 'Puerto-Rico':3, 'Canada':4, 'Germany':5, 'Outlying-US(Guam-USVI-etc)':6, 'India':7, 'Japan':8, 'Greece':9, 'South':10, 'China':11,                   'Cuba':12, 'Iran':13, 'Honduras':14, 'Philippines':15, 'Italy':16, 'Poland':17, 'Jamaica':18, 'Vietnam':19, 'Mexico':20, 'Portugal':21, 'Ireland':22, 'France':23,                  'Dominican-Republic':24, 'Laos':25, 'Ecuador':26, 'Taiwan':27, 'Haiti':28, 'Columbia':29, 'Hungary':30, 'Guatemala':31, 'Nicaragua':32, 'Scotland':33, 'Thailand':34, 'Yugoslavia':35,                  'El-Salvador':36, 'Trinadad&Tobago':37, 'Peru':38, 'Hong':39, 'Holand-Netherlands':40
      }



def remove_field_num(a, i):                                                                      #function to strip values
   names = list(a.dtype.names)
   new_names = names[:i] + names[i + 1:]
   b = a[new_names]
   return b

def remove_missing_values(data):
    temp = []
    for i in range(len(data)):
        for j in range(len(data[i])):
            if data[i][j] == '?':                                                                 #If a missing value '?' is encountered do not append the line to temp
                break;
            if j == (len(data[i]) - 1) and len(data[i]) == 15:
                temp.append(data[i])                                                              #Append the lines that do not contain '?'
    return temp

def create_labels(data):
    temp = []
    for i in range(len(data)):                                                                    #Iterate through the data
        j = len(data[i]) - 1                                                                      #Extract the labels
        if data[i][j] == '<=50K':
            temp.append(0)
        else:
            temp.append(1)
    return temp

def convert_to_int(data):

    my_lst = []
    for i in range(len(data)):
        lst = []
        for j in range(len(data[i])):
            key = data[i][j]
            if j in (1, 3, 5, 6, 7, 8, 9, 13, 14):
                lst.append(int(attributes[key]))
            else:
                lst.append(int(key))
        my_lst.append(lst)

    temp = np.array(my_lst)
    return temp

我尝试同时使用 tree 和 NaiveBayes ，但是准确性很低.有什么我想念的建议吗?

I have tried to use both tree and NaiveBayes but the accuracy is very low. Any suggestions of what am I missing?

推荐答案

我猜问题出在预处理上.最好将分类变量编码为one_hot向量(只有零的向量或其中一个对应于该类的期望值的向量)，而不是原始数字.Sklearn DictVectorizer 可以为您提供帮助.您可以使用 pandas 库更有效地进行分类.

I guess the problem is in preprocessing. It is better to encode the categorical variables as one_hot vectors (vectors with only zero or ones where one corresponds to the desired value for that class) instead of raw numbers. Sklearn DictVectorizer can help you in that. You can do the classification much more efficiently with the pandas library.

以下内容显示了借助 pandas 库可以轻松实现这一目标.它在scikit-learn方面效果很好.在占整个数据20％的测试集上，这可以达到81.6的准确性.

The following shows how easily you can achieve that with help of pandas library. It works very well along side scikit-learn. This achieves accuracy of 81.6 on a test set that is 20% of the entire data.

from __future__ import division

from sklearn.cross_validation import train_test_split
from sklearn.feature_extraction.dict_vectorizer import DictVectorizer
from sklearn.linear_model.logistic import LogisticRegression
from sklearn.metrics.classification import classification_report, accuracy_score
from sklearn.naive_bayes import GaussianNB
from sklearn.tree.tree import DecisionTreeClassifier

import numpy as np
import pandas as pd


# Read the data into a pandas dataframe
df = pd.read_csv('adult.data.csv')

# Columns names
cols = np.array(['age', 'workclass', 'fnlwgt', 'education', 'education-num',
                 'marital-status', 'occupation', 'relationship', 'race', 'sex',
                 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',
                 'target'])

# numeric columns
numeric_cols = ['age', 'fnlwgt', 'education-num',
                'capital-gain', 'capital-loss', 'hours-per-week']

# assign names to the columns in the dataframe
df.columns = cols

# replace the target variable to 0 and 1 for <50K and >50k
df1 = df.copy()
df1.loc[df1['target'] == ' <=50K', 'target'] = 0
df1.loc[df1['target'] == ' >50K', 'target'] = 1

# split the data into train and test
X_train, X_test, y_train, y_test = train_test_split(
    df1.drop('target', axis=1), df1['target'], test_size=0.2)


# numeric attributes

x_num_train = X_train[numeric_cols].as_matrix()
x_num_test = X_test[numeric_cols].as_matrix()

# scale to <0,1>

max_train = np.amax(x_num_train, 0)
max_test = np.amax(x_num_test, 0)        # not really needed

x_num_train = x_num_train / max_train
x_num_test = x_num_test / max_train        # scale test by max_train

# labels or target attribute

y_train = y_train.astype(int)
y_test = y_test.astype(int)

# categorical attributes

cat_train = X_train.drop(numeric_cols, axis=1)
cat_test = X_test.drop(numeric_cols, axis=1)

cat_train.fillna('NA', inplace=True)
cat_test.fillna('NA', inplace=True)

x_cat_train = cat_train.T.to_dict().values()
x_cat_test = cat_test.T.to_dict().values()

# vectorize (encode as one hot)

vectorizer = DictVectorizer(sparse=False)
vec_x_cat_train = vectorizer.fit_transform(x_cat_train)
vec_x_cat_test = vectorizer.transform(x_cat_test)

# build the feature vector

x_train = np.hstack((x_num_train, vec_x_cat_train))
x_test = np.hstack((x_num_test, vec_x_cat_test))


clf = LogisticRegression().fit(x_train, y_train.values)
pred = clf.predict(x_test)
print classification_report(y_test.values, pred, digits=4)
print accuracy_score(y_test.values, pred)

clf = DecisionTreeClassifier().fit(x_train, y_train)
predict = clf.predict(x_test)
print classification_report(y_test.values, pred, digits=4)
print accuracy_score(y_test.values, pred)

clf = GaussianNB().fit(x_train, y_train)
predict = clf.predict(x_test)
print classification_report(y_test.values, pred, digits=4)
print accuracy_score(y_test.values, pred)

这篇关于scikit在分类器上的准确性很低(朴素贝叶斯，DecissionTreeClassifier)的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！