[ML]² : Machine Learning for Machine Learning
ML Square is a python library that utilises deep learning techniques to
- Enable interoperability between existing standard machine learning frameworks.
- Provide explainability as a first-class function.
- Make ML self learnable.
mlsquare is simple and easy
- Create a Virtual Environment(optional)virtualenv ~/venv source ~/venv/bin/activate
mlsquarepackagepip install mlsquare
mlsquareand pass the
sklearnmodel object>>> from mlsquare import dope >>> from sklearn.linear_model import LinearRegression >>> from sklearn.preprocessing import StandardScaler >>> from sklearn.model_selection import train_test_split >>> import pandas as pd >>> from sklearn.datasets import load_diabetes >>> model = LinearRegression() >>> diabetes = load_diabetes() >>> X = diabetes.data >>> sc = StandardScaler() >>> X = sc.fit_transform(X) >>> Y = diabetes.target >>> x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.60, random_state=0) >>> m = dope(model) >>> # All sklearn operations can be performed on m, except that the underlying implementation uses DNN >>> m.fit(x_train, y_train) >>> m.score(x_test, y_test)
For a comprehensive tutorial please do checkout this link
To get started with contributing, refer our devoloper guide here
For detailed documentation refer documentation
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