Logistic Regression (Step by Step)

I’ve created these step-by-step machine learning algorith implementations in Python for everyone who is new to the field and might be confused with the different steps.

Logistic Regression is a very old model (think ~200ish years) that still works pretty well for many different problems.

Check out this page to learn about the history Logistic of Regression .

Its mathematical foundations, high accuracy and high scalability makes it favorable in many cases.

And on top of that, it produces statistical outputs and probability calculations and sometimes that’s just what you need (compared to just labels).

With the help of regularization, this model can be a strong member of the Machine Learning toolset when you have Data Science problems with linear decision boundary relations.

Check out the pros & cons of Logistic Regression to discover more.

Estimated Time

10 mins

Skill Level

Advanced

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I’ve split up Logistic Regression implementation to 2 different categories here:

(Red for the actual machine learning work and black font signifies preparation phase)

Down the page I’ve also color coded the steps in a different way to group similar steps with each other.

Import the relevant Python libraries
Import the data

Read / clean / adjust the data (if needed)
Create a train / test split
Create the Logistic Regression model object

Fit the model
Predict
Evaluate the accuracy

Let’s read more about each individual step and what’s achieved with each of them:

1 Import Libraries

pandas can be useful for constructing dataframes and scikit learn is the ultimate library for simple machine learning operations, learning and practicing machine learning.

3 Read the Data

Reading data is simple but there can be important points such as: dealing with columns, headers, titles, constructing data frames etc.

5 Create the Model

Machine Learning models can be created with a very simple and straight-forward process using scikitlearn. We will create a Logistic Regression object from the LogisticRegression of scikitlearn.linear_model library.

7 Predict

Once the model is ready, predictions can be done on the test part of the data. Furthermore, I enjoy predicting foreign values that are not in the initial dataset just to observe the outcomes the model creates. .predict method is used for predictions.

2 Import the Data

We need a nice dataset that’s sensible to analyze with machine learning techniques, particularly logistic regression in this case. Scikitlearn has some cool sample data as well.

4 Split the Data

Even splitting data is made easy with Scikit-learn, for this operation we will use train_test_module from scikitlearn library.

6 Fit the Model

Machine Learning models are generally fit by training data. This is the part where training of the model takes place and we will do the same for our Logistic Regression model.

8 Evaluation

Finally, scikitlearn library’s metrics module is very useful to test the accuracy of the model’s predictions. This part could be done manually as well but metrics module brings lots of functionality and simplicity to the table.

1- Importing the libraries (pandas and sklearn libraries)

First the import part for libraries:

pandas is imported for data frames
train_test_split from sklearn.model_selection makes splitting data for train and test purposes very easy and proper
sklearn.linear_model provides the actual model for Logistic Regression

datasets module of sklearn has great datasets making it easy to experiment with AI & Machine Learning
metrics is great for evaluating the results after training and prediction.

###Importing Libraries
import pandas as pd
from sklearn import datasets
from sklearn.linear_model import LogisticRegression as logreg
from sklearn.model_selection import train_test_split as tts
from sklearn import metrics

2- Importing the data (iris dataset)

It’s time to find some data to work with. For the simplicity I will suggest using pre-included datasets library in scikitlearn. They are great for practice and everything is already taken care. So, there won’t be a complication such as missing values or invalid characters etc. while you’re learning.

Let’s import the iris dataset:

###Importing Dataset
iris = datasets.load_iris()

3- Reading the data (scikitlearn datasets and pandas dataframe)

Now we can get the data ready:

Pandas DataFrame class is used to construct a data frame. Data frames are very useful when working with large datasets with different titles.

In case of machine learning algorithms: you usually have feature(s) and an outcome or multiple outcomes to work with, this mean different titles and sometimes different types of data. That’s why DataFrame becomes the perfect structure to work with.

###Constructing Data Frame
data = pd.DataFrame({"sl":iris.data[:,0], "sw":iris.data[:,1], "pl":iris.data[:,2], "pw":iris.data[:,3], 'species': iris.target})
# print(data["species"])

4- Splitting the data (train_test_split module)

This is another standard Machine Learning step.

We need to split data so that there are:

training feature(s) and outcome(s)
test feature(s) and test outcome(s)

The logic is to train the logistic regression machine learning model with the train split and then test the trained model with the test split.

It’s a rather simple process (step) thanks to Scikit learn’s train_test_split module.

I named the variables X_tr, y_tr for training and X_ts, y_ts for test input. This is up to your taste or your circumstances.
X_tr, X_ts will be assigned to a part of the features
y_tr, y_ts will be assigned to a part of outcomes

Split ratio can be assigned using test_size parameter. This is an important parameter and something you should experiment with to get a better understanding. 1/3rd or 30% usually are reasonable ratios.
Then model works on X_tr and y_tr for training.
Then we will test it on X_ts and y_ts to see how successful the model is.

###Splitting train/test data
x=data[['sl','sw','pl','pw']]
y=data["species"]
X_tr, X_ts, y_tr, y_ts = tts(X,y, test_size=30/100, random_state=None)

5- Creating the model (linear_model.LogisticRegression)

Now we can create a Logistic Regression object and put machine learning to work using the training data:

###Creating Logistic Regression Model
LOGR = logreg()

6- Fitting the model (Training with features(X) and outcomes (y))

###Training the Model
LOGR.fit(X_tr,y_tr)

7- Making predictions (.predict method)

###Making Predictions
y_pr=DT.predict(X_ts)
print(y_pr)

8- Evaluating results (scikitlearn metrics module)

###Evaluating Prediction Accuracy
print("Acc %:",metrics.accuracy_score(y_ts, y_pr)*100)

Bonus: Predicting foreign data

###Making Prediction with Foreign Data
print(DT.predict([[1,1,0.5,6]]))

You can see the full one piece code in this page: Logistic Regression Simple Implementation

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