learned 0.5.1

Package containing deep learning model, classic machine learning models, various preprocessing functions and result metrics

learned

Machine Learning library for Python (very soon C and JavaScript)

Table of Contents

• LinReg class

  • LinReg.train

  • LinReg.test

  • LinReg.predict

  • LinReg.r2_score

  • LinReg.intercept

  • LinReg.coefficients

• LogReg class

  • LogReg.train

  • LogReg.predict

• GradientDescent class

  • Parameters

  • GradientDescent.optimizer

  • GradientDescent.predict

  • GradientDescent.get_parameters

• Preprocessing class

  • Preprocessing.get_split_data

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