machlearn 1.0.0

Machine Learning Python Library

Machine Learning Python Library

Install

pip install machlearn

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Example 1: Model evaluation

from machlearn import model_evaluation as me
me.demo()

Selected Output:

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Example 2: Naive Bayes

from machlearn import naive_bayes as nb
nb.demo()

Selected Output:

This demo uses a public dataset of SMS spam, which has a total of 5574 messages = 747 spam and 4827 ham (legitimate).
The goal is to use 'term frequency in the message' to predict whether the message is ham (class=0) or spam (class=1).

Using test_size = 0.25 and training a multinomial naive bayes model, the best hyperparameters were found to be:
   Step1: Convert from text to count matrix = CountVectorizer(analyzer = __lemmas);
   Step2: Transform count matrix to tf-idf = TfidfTransformer(use_idf = True).

Application example:
   - Message: "URGENT! We are trying to contact U. Todays draw shows that you have won a 2000 prize GUARANTEED. Call 090 5809 4507 from a landline. Claim 3030. Valid 12hrs only."
   - Probability of class=1 (spam): 95.85%
   - Classification: spam

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module: model_evaluation

function	description
plot_confusion_matrix()	plots the confusion matrix, along with key statistics, and returns accuracy
plot_ROC_curve()	plots the ROC (Receiver Operating Characteristic) curve, along with statistics
plot_PR_curve()	plots the precision-recall curve, along with statistics
plot_ROC_and_PR_curves()	plots both the ROC and the precision-recall curves, along with statistics
demo()	provides a demo of the major functions in this module

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module: naive_bayes

function	description
naive_bayes_Bernoulli()	when X are independent binary variables (e.g., whether a word occurs in a document or not)
naive_bayes_multinomial()	when X are independent discrete variables with 3+ levels (e.g., term frequency in the document)
naive_bayes_Gaussian()	when X are continuous variables
demo()	provides a demo of selected functions in this module

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module: neural_network

function	description
rnn()	Recurrent neural network
demo()	provides a demo of selected functions in this module

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module: decision_tree

function	description
boost()	Boosting
demo()	provides a demo of selected functions in this module