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收集的数据格式不对（如 SQL 数据库、JSON、CSV 等） -
缺失值和异常值 -
标准化 -
减少数据集中存在的固有噪声（部分存储数据可能已损坏） -
数据集中的某些功能可能无法收集任何信息以供分析
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提高精度 -
降低过拟合风险 -
加快训练速度 -
改进数据可视化 -
增加我们模型的可解释性
X = df.drop([‘class’], axis = 1)
Y = df[‘class’]
X = pd.get_dummies(X, prefixsep=’’)
Y = LabelEncoder().fit_transform(Y)
X2 = StandardScaler().fit_transform(X)
X_Train, X_Test, Y_Train, Y_Test = train_test_split(X2, Y, test_size = 0.30, random_state = 101)
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start = time.process_time()
trainedforest = RandomForestClassifier(n_estimators=700).fit(X_Train,Y_Train)
print(time.process_time() - start)
predictionforest = trainedforest.predict(X_Test)
print(confusion_matrix(Y_Test,predictionforest))
print(classification_report(Y_Test,predictionforest))
figure(num=None, figsize=(20, 22), dpi=80, facecolor=‘w’, edgecolor=‘k’)
feat_importances = pd.Series(trainedforest.featureimportances, index= X.columns)
feat_importances.nlargest(7).plot(kind=‘barh’)
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X_Reduced = X[[‘odor_n’,‘odor_f’, ‘gill-size_n’,‘gill-size_b’]]
X_Reduced = StandardScaler().fit_transform(X_Reduced)
X_Train2, X_Test2, Y_Train2, Y_Test2 = train_test_split(X_Reduced, Y, test_size = 0.30, random_state = 101)
start = time.process_time()
trainedforest = RandomForestClassifier(n_estimators=700).fit(X_Train2,Y_Train2)
print(time.process_time() - start)
predictionforest = trainedforest.predict(X_Test2)
print(confusion_matrix(Y_Test2,predictionforest))
print(classification_report(Y_Test2,predictionforest))
start = time.process_time()
trainedtree = tree.DecisionTreeClassifier().fit(X_Train, Y_Train)
print(time.process_time() - start)
predictionstree = trainedtree.predict(X_Test)
print(confusion_matrix(Y_Test,predictionstree))
print(classification_report(Y_Test,predictionstree))
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import graphviz
from sklearn.tree import DecisionTreeClassifier, export_graphviz
data = export_graphviz(trainedtree,out_file=None,feature_names= X.columns,
class_names=[‘edible’, ‘poisonous’],
filled=True, rounded=True,
max_depth=2,
special_characters=True)
graph = graphviz.Source(data)
graph
from sklearn.feature_selection import RFE
model = RandomForestClassifier(n_estimators=700)
rfe = RFE(model, 4)
start = time.process_time()
RFE_X_Train = rfe.fit_transform(X_Train,Y_Train)
RFE_X_Test = rfe.transform(X_Test)
rfe = rfe.fit(RFE_X_Train,Y_Train)
print(time.process_time() - start)
print(“Overall Accuracy using RFE: ”, rfe.score(RFE_X_Test,Y_Test))
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from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
model = ExtraTreesClassifier()
start = time.process_time()
model = model.fit(X_Train,Y_Train)
model = SelectFromModel(model, prefit=True)
print(time.process_time() - start)
Selected_X = model.transform(X_Train)
start = time.process_time()
trainedforest = RandomForestClassifier(n_estimators=700).fit(Selected_X, Y_Train)
print(time.process_time() - start)
Selected_X_Test = model.transform(X_Test)
predictionforest = trainedforest.predict(Selected_X_Test)
print(confusion_matrix(Y_Test,predictionforest))
print(classification_report(Y_Test,predictionforest))
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如果两个特征之间的相关性为 0，则意味着更改这两个特征中的任何一个都不会影响另一个。 -
如果两个特征之间的相关性大于 0，这意味着增加一个特征中的值也会增加另一个特征中的值（相关系数越接近 1，两个不同特征之间的这种联系就越强）。 -
如果两个特征之间的相关性小于 0，这意味着增加一个特征中的值将使减少另一个特征中的值（相关性系数越接近-1，两个不同特征之间的这种关系将越强）。
Numeric_df = pd.DataFrame(X)
Numeric_df[‘Y’] = Y
corr= Numeric_df.corr()
corr_y = abs(corr[“Y”])
highest_corr = corr_y[corr_y >0.5]
highest_corr.sort_values(ascending=True)
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figure(num=None, figsize=(12, 10), dpi=80, facecolor=‘w’, edgecolor=‘k’)
corr2 = Numeric_df[[‘bruises_f’ , ‘bruises_t’ , ‘gill-color_b’ , ‘gill-size_b’ , ‘gill-size_n’ , ‘ring-type_p’ , ‘stalk-surface-below-ring_k’ , ‘stalk-surface-above-ring_k’ , ‘odor_f’, ‘odor_n’]].corr()
sns.heatmap(corr2, annot=True, fmt=“.2g”)
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Classification = chi2, f_classif, mutual_info_classif -
Regression = f_regression, mutual_info_regression
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
min_max_scaler = preprocessing.MinMaxScaler()
Scaled_X = min_max_scaler.fit_transform(X2)
X_new = SelectKBest(chi2, k=2).fit_transform(Scaled_X, Y)
X_Train3, X_Test3, Y_Train3, Y_Test3 = train_test_split(X_new, Y, test_size = 0.30, random_state = 101)
start = time.process_time()
trainedforest = RandomForestClassifier(n_estimators=700).fit(X_Train3,Y_Train3)
print(time.process_time() - start)
predictionforest = trainedforest.predict(X_Test3)
print(confusion_matrix(Y_Test3,predictionforest))
print(classification_report(Y_Test3,predictionforest))
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from sklearn.linear_model import LassoCV
regr = LassoCV(cv=5, random_state=101)
regr.fit(X_Train,Y_Train)
print(“LassoCV Best Alpha Scored: “, regr.alpha_)
print(“LassoCV Model Accuracy: “, regr.score(X_Test, Y_Test))
modelcoef = pd.Series(regr.coef, index = list(X.columns[:-1]))
print(“Variables Eliminated: “, str(sum(model_coef == 0)))
print(“Variables Kept: “, str(sum(model_coef != 0)))
figure(num=None, figsize=(12, 10), dpi=80, facecolor=‘w’, edgecolor=‘k’)
top_coef = model_coef.sort_values()
top_coef[top_coef != 0].plot(kind = “barh”)
plt.title(“Most Important Features Identified using Lasso (!0)”)
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