This time I am putting together the commonly used scikit-learn packages as a memo
K-nearest neighbors:
from sklearn.neighbors import KNeighborsClassifier
Linear regression:
from sklearn.linear_model import LinearRegression
Logistic regression:
from sklearn.linear_model import LogisticRgression
Decision tree:
from sklearn.tree import DecisionTreeClassifier
Naive Bayes:
- Gaussian distribution: from sklearn.neive_bayes import GaussianNB
- Multinomial distribution: from sklearn.neive_baybes import MultinomnalNB
- Bernoulli distribution: from sklearn.neive_baybes import BernoulliNB
Support vector machine:
- Classification: from sklearn.svm import SVC
- Regression: from sklearn.svm import SVR
Ensemble algorithms (sklearn.ensemble):
- bagging:
Classification: from sklearn.ensemble import BaggingClassifier
Regression: from sklearn.ensemble import BaggingRegression
- AdaBoost:
Classification: from sklearn.ensemble import AdaBoostClassifier
Regression: from sklearn.ensemble import AdaBoostRegression
- Random forest:
Classification: from sklearn.ensemble import RandomForestClassifier
Regression: from sklearn.ensemble import RandomForestRegression
K-means:
from sklearn.cluster import KMeans
PCA dimensionality reduction:
from sklearn.decomposition import PCA
Finally, a scikit treasure map: