[빅데이터분석기사] 실기 - sklearn

 

1. Linear Model

from sklearn.linear_model import *

 

< Classifier >

 

LogisticRegression

RidgeClassifier

 

< Regression >

 

LinearRegression

Ridge

Lasso

ElasticNet

 

 

2. Decomposition

from sklearn.decomposition

 

PCA

 

 

3. Ensemble

from sklearn.ensemble import *

 

BaggingClassifier

BaggingRegressor

GradientBoostingClassifier

GradientBoostingRegressor

RandomForestClassifier

RandomForestRegressor

VotingClassifier

VotingRegressor

 

 

4. Preprocessing and Normalization

from sklearn.preprocessing import *

 

LabelEncoder

MinMaxScaler

Normalizer

StandardScaler

RobustScaler

 

 

5. Model selection

from sklearn.model_selection import *

 

KFold

cross_val_score

 

 

6. tree

from sklearn.tree import *

 

DecisionTreeClassifier

DecisionTreeRegressor

 

 

7. cluster

from sklearn.cluster import *

 

DBSCAN

KMeans

 

 

8. Scoring

from sklearn.metrics import *

 

< classification >

 

accuracy_score

f1_score

log_loss

precision_score

recall_score

roc_auc_score

 

< Regression >

 

mean_absolute_error

mean_squared_error

mean_squared_log_error

 

 

 

 

 

https://scikit-learn.org/stable/modules/classes.html#module-sklearn.preprocessing

API Reference — scikit-learn 0.24.2 documentation