Binary Classification with Python

In the previous two examples, I worked on Multi-Class Classification problems, wherein the class (output) can be one of the multiple values.

In this example, I took up a Binary Classification problem where the output is either 1 or 0.

The approach is similar to previous two examples, so I would only highlight on important points.

For the Binary Classification program, the data set taken is Pima Indians Diabetes Database. In this data set, there are 8 features:

  1. Number of times pregnant
  2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test
  3. Diastolic blood pressure (mm Hg)
  4. Triceps skin fold thickness (mm)
  5. 2-Hour serum insulin (mu U/ml)
  6. Body mass index (weight in kg/(height in m)^2)
  7. Diabetes pedigree function
  8. Age (years)

The output is Class variable with values 0 or 1. Class 1 represent tested positive for diabetes. There are 768 rows in this data set.

The accuracy for different set of algorithms for this classification looks like this:

As can be seen, Logistic Regression and Linear Discriminant Analysis shows a better accuracy compared to others.

If I plot it as a Whisker and Box,

For final validation, first I used Logistic Regression and this is the result:

So the accuracy stands at ~ 78%.

When I used Linear Discriminant Analysis, the result looks like this:


As expected, there is no difference in accuracy compared to Logistic Regression.

The summary of this exercise is - for this Classification Problem, either Logistic Regression or Linear Discriminant Analysis seems to be the better algorithms. The solution approach is similar to what I followed for Multi-Class Classification problem.

The Source Code is available in Github.

In this exercise, there was one more learning which I will share in my next post.

Comments

Post a Comment

Popular posts from this blog

Ordinal Encoder, OneHotEncoder and LabelBinarizer in Python

Usually in the data set we work on, we will have both numerical data as well as categorical data (non-numeric). There are algorithms which do not work on categorical data. These algorithms are designed that way to enhance efficiency. So in such cases how do we train the model with categorical data? This is where we need to apply a technique called Encoding. Encoding transforms categorical data into numeric. There are various Encoders available in sci-kit learn library. In this post, I'll share my learning on three Encoders. Ordinal Encoder This performs ordinal (integer) encoding of categorical data. Let's look at an example. I have an array of categorical data. This is a 2 dimensional array. multiArray = np.array([[ 'Karnataka' ,  'KA' ], [ 'Maharastra' ,  'MH' ], [ 'Gujarat' ,  'GJ' ]]) Then I use OrdinalEncoder to transform this data. ordinalEncoder = OrdinalEncoder() ordinalEncoderArray = ordinalEncoder.fit_transform(mult...
Read more

Data Visualization using Pandas - Univariate Plots

Image
Data visualization is an important step in ML. That helps us to understand the data in visual representation. Why does it matter? Because it helps us to understand the distribution of the data, any outliers, and also relationship between multiple attributes. Experts also mention that understanding the distribution helps us in choosing correct algorithm. I cannot comment on this statement as of now, as I do not have enough knowledge on that. When I reach that stage, I’ll definitely share. Pandas library provides methods to plot data in multiple ways. In fact, I did use one of those plots called  Box and Whisker Plots  in my previous examples. However, those plots were used to see the difference between multiple algorithms. In this post, I’ll share how to visualize the data which is an important step much before we apply any ML algorithm. I’ll use the data set which was used for  Binary Classification . The data set is   Pima Indians Diabetes Database  which ...
Read more

Stemming in Python

While working on NLP, we come across huge volume of words. Before using these words to the ML algorithm, a lot of pre-processing happens. These pre-processing ensures we keep relevant words which can then be used to train an ML algorithm. One such technique is called Stemming . As defined in Wikipedia - "In linguistics, a stem is a part of a word used with slightly different meanings and would depend on the morphology of the language in question." A simple example is the words approximation , approximate , approximated and approximately have the same root. So as part of Stemming, we need to make sure these words should be taken as a single word instead of four different words. So how do we achieve this in Python? The NLTK library provides support for Stemming. There are two algorithms for stemming - PorterStemmer and LancasterStemmer . I'll not get into the details of how these two works, but at a high level LancasterStemmer does a lot more iterations compared to Porte...
Read more