Package containing deep learning model, classic machine learning models, various preprocessing functions and result metrics
Project description
learned
Machine Learning library for Python (very soon C and JavaScript)
Table of Contents
LinReg class
Explanation:
LinReg is a class that allows simple or multiple linear regressions and returns trained parameters.
Parameters:
data: Unfragmented structure that contains inputs and outputs.
Usage:
'''
// The "full_dataset" is an unfragmented structure that contains inputs and outputs.
lin_reg = Learn.LinReg(data=full_dataset) # or lin_reg = LinReg(full_dataset)
'''
Output:
'''
<Learn.LinReg at 0x1fdbd6b6220>
'''
LinReg.train
Explanation:
It applies the training process for the dataset entered while creating the class.
Parameters:
This method does not take parameter!
Usage:
'''
lin_reg.train()
'''
Output:
(An example simple linear regression output)
'''
Completed in 0.0 seconds.
Training R2-Score: % 97.0552464372771
Intercept: 10349.456288746507, Coefficients: [[812.87723722]]
'''
LinReg.test
Explanation:
Applies the created model to a different input and gives the r2 score result.
Parameters:
t_data: Unfragmented structure that contains inputs and outputs.
Usage:
'''
lin_reg.test(t_data=test_dataset) # or lin_reg.test(test_dataset)
'''
Output:
(An example simple linear regression output)
'''
Testing R2-Score: % 91.953582170654
'''
Note:
Returns an error message if applied for a model that has not been previously trained.
'''
Exception: Model not trained!
'''
LinReg.predict
Explanation:
Applies the created model to the input data, which it takes as a parameter, and returns the estimated results.
Parameters:
x: Input dataset consisting of arguments
Usage:
'''
predicts = lin_reg.predict(x=x_set) # or predicts = lin_reg.predict(x_set)
'''
Output:
Predicted values list
Note:
Returns an error message if applied for a model that has not been previously trained.
'''
Exception: Model not trained!
'''
LinReg.r2_score
Explanation:
It takes actual results and predicted results for the same inputs as parameters and returns the value of r2 score.
Parameters:
y_true: Real results
y_predict: Estimated results
Usage:
'''
lin_reg.r2_score(y_true=real_results, y_predict=predicted_results) # or lin_reg.r2_score(real_results, predicted_results)
'''
Output:
'''
0.970552
dtype: float64
'''
LinReg.intercept
Explanation:
Returns the trained intercept value
Parameters:
@property (Does not take parameter)
Usage:
'''
intercept = lin_reg.intercept
'''
Output:
'''
10349.456288746507
'''
LinReg.coefficients
Explanation:
Returns the trained coefficients
Parameters:
@property (Does not take parameter)
Usage:
'''
coefficients = lin_reg.coefficients
'''
Output:
'''
array([[812.87723722]])
'''
LogReg class
Parameters
LogReg.train
LogReg.predict
GradientDescent class
Parameters
GradientDescent.optimizer
GradientDescent.predict
GradientDescent.get_parameters
Preprocessing class
Preprocessing.get_split_data
TODO
- cross validation
- p-value
- Other algorithms
- Detailed documentation
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