AI News, Machine Learning in Javascript: Introduction

Machine Learning in Javascript: Introduction

Instead, they rely on Python libraries to do the work for them, and end up not truly grasping what's happening inside the black box.

Through this series of articles, I'll teach you the fundamental machine learning algorithms using Javascript -- not Python or Octave -- as the example language.

Originally I intended to write these articles in a variety of languages (PHP, JS, Perl, C, Ruby), but decided to stick with Javascript for the following reasons: It's possible to get excellent performance out of ML algorithms in languages like PHP and Javascript.

I advocate writing ML algorithms in other languages because the practice of writing ML algorithms from scratch helps you learn them fundamentally, and it also helps you unify your backend by not requiring a Python script to do processing in the middle of a PHP application.

The advantage of NumPy or Matlab is not in their ability to do matrix operations, it's in the fact that they use optimized algorithms to do so -- things you wouldn't be able to do yourself unless you dedicate yourself to learning computational linear algebra.

Confusion Matrix in Machine Learning

In the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as an error matrix. A

confusion matrix is a table that is often used to describe the performance of a classification model (or “classifier”) on a set of test data for which the true values are known.

Low recall, high precision:This shows that we miss a lot of positive examples (high FN) but those we predict as positive are indeed positive (low FP) F-measure:Since we have two measures (Precision and Recall) it helps to have a measurement that represents both of them.

Let’s consider an example now, in which we have infinite data elements of class B and a single element of class A and the model is predicting class A against all the instances in the test data. Here, Precision

= TP / (TP + FP)=100/ (100+10)=0.91 F-measure:Fmeasure=(2*Recall*Precision)/(Recall+Presision)=(2*0.95*0.91)/(0.91+0.95)=0.92 Here is a python script which demonstrates how to create a confusion matrix on a predicted model.For this, we have to import confusion matrix module from sklearn library which helps us to generate the confusion matrix.

precision recall f1-score support

0 0.80 0.67 0.73 6

1 0.60 0.75 0.67 4 avg

/ total 0.72 0.70 0.70 10

Machine Learning in Javascript: Introduction

Instead, they rely on Python libraries to do the work for them, and end up not truly grasping what's happening inside the black box.

Through this series of articles, I'll teach you the fundamental machine learning algorithms using Javascript -- not Python or Octave -- as the example language.

Originally I intended to write these articles in a variety of languages (PHP, JS, Perl, C, Ruby), but decided to stick with Javascript for the following reasons: It's possible to get excellent performance out of ML algorithms in languages like PHP and Javascript.

I advocate writing ML algorithms in other languages because the practice of writing ML algorithms from scratch helps you learn them fundamentally, and it also helps you unify your backend by not requiring a Python script to do processing in the middle of a PHP application.

The advantage of NumPy or Matlab is not in their ability to do matrix operations, it's in the fact that they use optimized algorithms to do so -- things you wouldn't be able to do yourself unless you dedicate yourself to learning computational linear algebra.

How to Analyze Tweet Sentiments with PHP Machine Learning

Some examples of classification applications are: Machine learning is something of an umbrella term that covers many generic algorithms for different tasks, and there are two main algorithm types classified on how they learn –

In supervised learning, we train our algorithm using labelled data in the form of an input object (vector) and a desired output value;

the algorithm analyzes the training data and produces what is referred to as an inferred function which we can apply to a new, unlabelled dataset.

To exemplify the process of implementing PHP-ML and adding some machine learning to our applications, I wanted to find a fun problem to tackle and what better way to showcase a classifier than building a tweet sentiment analysis class.

The raw dataset has the following columns: And looks like following example (side-scrollable table): The file contains 14,640 tweets, so it’s a decent dataset for us to work with.

To make sure we are set up correctly, let’s create a quick script that will load our Tweets.csv data file and make sure it has the data we need.

Copy the following code as reviewDataset.php in the root of our project: Now, run the script with php reviewDataset.php, and let’s review the output: Now that doesn’t look useful, does it?

Let’s take a look at the CsvDataset class to get a better idea of what’s happening internally: The CsvDataset constructor takes 3 arguments: If we look a little closer we can see that the class is mapping out the CSV file into two internal arrays: samples and targets.

Based on the above, we can see that the format our CSV file needs to follow is as follows: We will need to generate a clean dataset with only the columns we need to continue working.

For this example, we are going to make use of the following two classes: Let’s start with our text vectorizer: Next, apply the Tf-idf Transformer: Our samples array is now in a format where it an easily be understood by our classifier.

a little bit and split our original dataset into two: a training dataset and a much smaller dataset that will be used for testing the accuracy of our model.

The term comes from statistics and can be defined as follows: Cross-validation, sometimes called rotation estimation, is a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set.

To implement our sentiment analysis class, we have three classification algorithms available: For this exercise we are going to use the simplest of them all, the NaiveBayes classifier, so let’s go ahead and update our class to implement the train method: As you can see, we are letting PHP-ML do all the heavy lifting for us.

Let’s take a look at the code: We should see something along the lines of: This article fell a bit on the long side, so let’s do a recap of what we’ve learned so far: This post also served as an introduction to the PHP-ML library and hopefully gave you a good idea of what the library can do and how it can be embedded in your own projects.

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