File size: 2,741 Bytes
d7e53e8 4928a1a d7e53e8 4928a1a d7e53e8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | from sklearn.linear_model import LinearRegression, LogisticRegression, Perceptron
from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.svm import SVC, SVR
from sklearn.neural_network import MLPClassifier, MLPRegressor
from sklearn.ensemble import (
RandomForestClassifier, RandomForestRegressor,
ExtraTreesClassifier, ExtraTreesRegressor,
AdaBoostClassifier, AdaBoostRegressor,
GradientBoostingClassifier, GradientBoostingRegressor,
StackingClassifier, StackingRegressor
)
REGRESSION_MODELS = {
"Linear Regression": LinearRegression(),
"KNN Regressor": KNeighborsRegressor(),
"Decision Tree Regressor": DecisionTreeRegressor(),
"SVR": SVR(),
"MLP Regressor": MLPRegressor(max_iter=1000),
}
CLASSIFICATION_MODELS = {
"Logistic Regression": LogisticRegression(max_iter=500),
"KNN Classifier": KNeighborsClassifier(),
"Naive Bayes": GaussianNB(),
"Perceptron": Perceptron(),
"Decision Tree Classifier": DecisionTreeClassifier(),
"SVM Classifier": SVC(probability=True),
"MLP Classifier": MLPClassifier(max_iter=1000),
}
MODEL_GROUPS = {
"Basic": {
"Regression": REGRESSION_MODELS,
"Classification": CLASSIFICATION_MODELS,
},
"Bagging": {
"Regression": {
"Random Forest Regressor": RandomForestRegressor(),
"Extra Trees Regressor": ExtraTreesRegressor(),
},
"Classification": {
"Random Forest Classifier": RandomForestClassifier(),
"Extra Trees Classifier": ExtraTreesClassifier(),
},
},
"Boosting": {
"Regression": {
"AdaBoost Regressor": AdaBoostRegressor(),
"Gradient Boosting Regressor": GradientBoostingRegressor(),
},
"Classification": {
"AdaBoost Classifier": AdaBoostClassifier(),
"Gradient Boosting Classifier": GradientBoostingClassifier(),
},
},
"Stacking": {
"Regression": {
"Stacking Regressor": StackingRegressor(
estimators=[("lr", LinearRegression())]
),
},
"Classification": {
"Stacking Classifier": StackingClassifier(
estimators=[("lr", LogisticRegression(max_iter=500))]
),
},
},
}
CLASSIFICATION_GRAPHS = [
"Confusion Matrix",
"ROC Curve",
"Per-Class Metrics Table",
"Precision-Recall Curve",
"Probability Histogram",
]
REGRESSION_GRAPHS = [
"Actual vs Predicted",
"Residual Plot",
"Residual Histogram",
"Feature Importance",
"Learning Curve",
] |