Logistic regression

Logistic regression is one of the most popular machine learning algorithms for binary classification.


Now we create some random data that are split into two different classes, ‘class 0’ and ‘class 1’.

We will use these data as a training set for logistic regression.

Import your data

This dataset represents 100 samples classified in two classes as 0 or 1 (stored in the third column), according to two parameters (stored in the first and second column):




Directly import your data in Scilab with the following command:




These data has been generated randomly by Scilab with the following script:

b0 = 10;
t = b0 * rand(100,2);
t = [t 0.5+0.5*sign(t(:,2)+t(:,1)-b0)];

b = 1;
flip = find(abs(t(:,2)+t(:,1)-b0)<b);
The data from different classes overlap slightly. The degree of overlapping is controlled by the parameter b in the code.

Represent your data

Before representing your data, you need to split them into two classes t0 and t1 as followed:

t0 = t(find(t(:,$)==0),:);
t1 = t(find(t(:,$)==1),:);
Then simply plot them:



Build a classification model

We want to build a classification model that estimates the probability that a new, incoming data belong to the class 1.

First, we separate the data into features and results:

x = t(:, 1:$-1); y = t(:, $);
[m, n] = size(x);

Then, we add the intercept column to the feature matrix

// Add intercept term to x
x = [ones(m, 1) x];
The logistic regression hypothesis is defined as:

h(θ, x) = 1 / (1 + exp(−θTx) )


It’s value is the probability that the data with the features x belong to the class 1.


The Cost Function in logistic regression is

J = [−yT log(h) − (1−y)T log(1−h)]/m

where log is the “element-wise” logarithm, not a matrix logarithm.


Gradient descent

If we use the gradient descent algorithm, then the update rule for the θ is

θ → θ − α ∇J = θ − α xT (h − y) / m

The code is as follows:

// Initialize fitting parameters
theta = zeros(n + 1, 1);

// Learning rate and number of iterations

a = 0.01;
n_iter = 10000;

for iter = 1:n_iter do
z = x * theta;
h = ones(z) ./ (1+exp(-z));
theta = theta – a * x’ *(h-y) / m;
J(iter) = (-y’ * log(h) – (1-y)’ * log(1-h))/m;

Visualize the results

Now, the classification can be visualized:

// Display the result

u = linspace(min(x(:,2)),max(x(:,2)));


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