コード例 #1
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labelencoder_X_1 = LabelEncoder()
X[:,1]=labelencoder_X_1.fit_transform(X[:,1])
labelencoder_X_2 = LabelEncoder()
X[:,2]=labelencoder_X_2.fit_transform(X[:,2])
onehotencoder = OneHotEncoder(categorical_features =[1])
X=onehotencoder.fit_transform(X).toarray()
X=X[:,1:]

#splitting
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)

#FeatureScaling
from sklearn.preprocessing import StandardScaler
sc =StandardScaler()
X_train = sc.fit_tranform(X_train)
X_test = sc.transform(X_test)

#Making ANN
#Importing Keras libraries and packages
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
#Initializing ANN
classifier = Sequential()

#Adding input layer and first hidden layer
classifier.add(Dense(output_dim=6,init='uniform',activation='relu',input_dim=11))
classifier.add(Dropout(p=0.1))
コード例 #2
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# Splitting the dataset into the Training set and Test set
from sklearn.cross_validation import train_test_split
trainx, testx, trainy, testy = train_test_split(X, y, test_size = 0.2, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sx = StandardScaler()
trainx = sx.fit_transform(trainx)
trainx = sx.transform(trainx)
x = sx.fit_transform(x)
sy = StandardScaler()
trainy = sy.fit_transform(trainy)
y = y.reshape(-1, 1)
y = sy.fit_tranform(y)

# Predicting the Test set results
predicty = regressor.predict(x/6.5)

# Correlation / Feature Importance
import seaborn as sns
sns.set(style="white")
sns.set(style="whitegrid", color_codes=True)

# Correlation Heatmap / Visualization
dataset.corr()
sns.heatmap(dataset.corr())


# Scatter Plot Visualization
コード例 #3
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@author:Wang Yan
@ide:PyCharm
@time:2019/5/8 21:10
"""
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
wine = load_wine()
print wine.target
print wine.data.shape
x_train, x_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.3)
sc = StandardScaler()
# 根据对之前部分trainData进行fit的整体指标,对剩余的数据(testData)使用同样的均值、方差、最大最小值等指标进行
# 转换transform(testData),从而保证train、test处理方式相同。
sc.fit_tranform(x_train)
sc.tranform(x_test)