Jx-MLT : A Machine Learning Toolbox for Regression

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"Toward Talent Scientist: Sharing and Learning Together" --- Jingwei Too

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Introduction

• This toolbox contains 7 widely used machine learning algorithms
• The Demo_LR and Demo_LASSO provide the examples of how to use these methods on benchmark dataset

Usage

You may switch the algorithm by changing the lr in from MLR.lr import jkfold to other abbreviations

• If you wish to use linear regression ( LR ) then you may write

from MLR.lr import jkfold 

• If you want to use decision tree ( DT ) then you may write

from MLR.dt import jkfold  

Input

• feat : feature vector matrix ( Instance x Features )
• label : label matrix ( Instance x 1 )
• opts : parameter settings
  • ho : ratio of testing data in hold-out validation
  • kfold : number of folds in k-fold cross-validation

Output

• mdl : Machine learning model ( It contains several results )
  • mse : mean square error
  • r2 : R square score

How to choose the validation scheme?

There are three types of performance validations. These validation strategies are listed as following ( LR is adopted as an example ).

• Hold-out cross-validation

from MLR.lr import jho

• K-fold cross-validation

from MLR.lr import jkfold

• Leave-one-out cross-validation

from MLR.lr import jloo

Example 1 : Linear Regression ( LR ) with k-fold cross-validation

import numpy as np
# change this to switch algorithm & types of validation (jho, jkfold, jloo)
from MLR.lr import jkfold 
import matplotlib.pyplot as plt
from sklearn import datasets


# load data
X, Y  = datasets.load_diabetes(return_X_y=True)
feat  = X[:, np.newaxis, 2]
label = Y

# parameters
kfold = 10
opts  = {'kfold':kfold}
# LR with k-fold
mdl   = jkfold(feat, label, opts) 

# overall mse
mse = mdl['mse']

# overall r2 score
r2  = mdl['r2']

Example 2 : LASSO Regression with hold-out validation

import numpy as np
# change this to switch algorithm & types of validation (jho, jkfold, jloo)
from MLR.lasso import jho 
import matplotlib.pyplot as plt
from sklearn import datasets


# load data
X, Y  = datasets.load_diabetes(return_X_y=True)
feat  = X[:, np.newaxis, 2]
label = Y

# parameters
ho    = 0.3    # ratio of testing data
alpha = 1
opts  = {'alpha':alpha, 'ho':ho}
# LR 
mdl   = jho(feat, label, opts) 

# overall mse
mse = mdl['mse']

# overall r2 score
r2  = mdl['r2']

Example 3 : Decision Tree ( DT ) with leave-one-out validation

import numpy as np
# change this to switch algorithm & types of validation (jho, jkfold, jloo)
from MLR.dt import jloo 
import matplotlib.pyplot as plt
from sklearn import datasets


# load data
X, Y  = datasets.load_diabetes(return_X_y=True)
feat  = X[:, np.newaxis, 2]
label = Y

# parameters
maxDepth = 5      # maximum depth of tree
opts  = {'maxDepth':maxDepth}
# DT 
mdl   = jloo(feat, label, opts) 

# overall mse
mse = mdl['mse']

# overall r2 score
r2  = mdl['r2']

Requirement

• Python 3
• Numpy
• Pandas
• Scikit-learn
• Matplotlib

List of available machine learning methods

• Click on the name of algorithm to check the parameters
• Use the opts to set the specific parameters
• If you do not set extra parameters then the algorithm will use default setting in here

No.	Abbreviation	Name
07	en	Elastic Net
06	nn	Neural Network
05	svr	Support Vector Regression
04	ridge	Ridge Regression
03	lasso	Lasso Regression
02	dt	Decision Tree
01	lr	Linear Regression