automl_tools
Project description
Automl_tools: automl binary classification
Automl_tools is a Python library that implements Gradient Boosting
Installation
pip install automl-tools
Usage
Probabilistic binary example on the Boston housing dataset:
import pandas as pd
from automl_tools.main import automl_run
train = pd.read_csv("https://raw.githubusercontent.com/jonaqp/automl_tools/main/automl_tools/examples/train.csv?token=AAN2ZBGCYYR7PATAMC6NIKDABSDCQ", sep=";")
test = pd.read_csv("https://raw.githubusercontent.com/jonaqp/automl_tools/main/automl_tools/examples/test.csv?token=AAN2ZBBD63PDQLGJNUWVHOLABSC4O", sep=";")
automl_run(train=train,
test=test,
target_col="Survived",
imp_num="knn",
imp_cat="knn",
processing="binding",
mutual_information=False,
correlation_drop=False,
model_feature_selection=None,
model_run="LR",
augmentation=True,
Stratified=True)
Parameter
imp_num : "gaussian", "arbitrary", "median", "mean", "random", "knn"
imp_cat : "frequent", "constant", "rare", "knn"
processing: "woe", "binding"
Support Binary
model_feature_selection:
default: ["LR", "RF", "LGB"]
LR : LogisticRegression
RF : RandomForestClassifier
SVM : SVC
LS : LASSO
RD : RIDGE
NET : Elasticnet
DT : DecisionTreeClassifier
ET : ExtraTreesClassifier
GB : GradientBoostingClassifier
AB : AdaBoostClassifier
XGB : XGBClassifier
LGB : LGBMClassifier
CTB : CatBoostClassifier
NGB : NGBClassifier
model_run:
default: "LR"
LR : LogisticRegression
RF : RandomForestClassifier
SVM : SVC
LS : LASSO
RD : RIDGE
NET : Elasticnet
DT : DecisionTreeClassifier
ET : ExtraTreesClassifier
GB : GradientBoostingClassifier
AB : AdaBoostClassifier
XGB : XGBClassifier
LGB : LGBMClassifier
CTB : CatBoostClassifier
NGB : NGBClassifier
License
New features v1.0
- multi_class
- regression
- integrations GCP deploy model CI/CD
- integrations AWS deploy model CI/CD
BugFix
- 0.1.2
- add parameter id_col
- add comments readme.txt
Reference
- Jonathan Quiza github.
- Jonathan Quiza RumiMLSpark.
- Jonathan Quiza linkedin.
Project details
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