Logistic Regression: Decoding the Binary

Table of Contents


Welcome, fellow explorers, to the world of logistic regression! You might be wondering, “What’s this new beast in the jungle of machine learning?” Well, don’t worry, we’re here to tame this beast together!

Just like how a switch has only two positions – on or off, many questions in our lives have only two answers – yes or no. For instance, will it rain tomorrow? The answer can either be yes, it will rain, or no, it won’t. And this is where logistic regression comes into the picture. It’s a super tool that helps us answer such yes or no questions.

In our previous explorations, we learned about different types of regression, where we tried to predict a number. For example, we used linear regression to predict the price of a house based on its features. But, what if we want to predict something that’s not a number, but a category, like yes or no, true or false, pass or fail? This is when we use logistic regression.

By the end of this article, you’ll know how to use logistic regression to solve such binary problems. You’ll learn how it works, how to interpret its results, and even apply it to a real-world problem. So, buckle up for this exciting journey to decode the binary world with logistic regression!


Before we dive into the ocean of logistic regression, let’s take a step back and remember our adventures in regression. We’ve already explored different types of regression like linear, ridge, and lasso, remember? These are great for predicting a number, like the price of a house or a person’s weight.

However, there are situations where we want to predict something that can be either yes or no. For example, will a given email be spam or not? Will a student pass or fail? These are binary outcome variables, meaning they have only two possible values – yes or no, or in technical terms, 0 or 1.

Now, you might be thinking, “Why not use linear regression for these tasks too?” Well, that’s a great question! Let’s think about it. Linear regression works well when the outcome we’re predicting is a number that can range from negative infinity to positive infinity. But in a binary classification task, our outcome variable can only be 0 or 1. If we try to fit a line, like in linear regression, we might predict values less than 0 or greater than 1, which doesn’t make sense in a yes or no scenario.

This is like trying to fit a square peg into a round hole – it just won’t work! So, we need a different approach when we’re dealing with binary outcome variables, and that’s where logistic regression comes to our rescue.


Have you ever wondered how your email knows whether to place an incoming message in your inbox or in the spam folder? Or how your bank’s system can predict fraudulent transactions amongst hundreds of thousands of legitimate ones? These are examples of binary classification problems, and one way to solve them is through logistic regression.

Explanation of Logistic Function and Sigmoid Curve

Picture yourself on a seesaw. You’re trying to balance but the seesaw keeps moving between two positions: up and down. This is similar to how logistic regression works! It’s constantly balancing between two outcomes. But instead of using a seesaw, logistic regression uses a special curve called the sigmoid curve or logistic function.

This curve looks like a smooth, continuous ‘S’ and ranges between 0 and 1. It’s very useful because it can take any number, no matter how big or small, and convert it to a value between 0 and 1. This is perfect for binary classification problems because we can interpret these 0 and 1 values as probabilities of belonging to a certain class! For example, a spam detection system might output a 0.9 for a suspicious email, meaning it thinks there’s a 90% chance the email is spam.

Understanding the Concept of Odds and Log Odds

Now let’s play a little game. Imagine you’re rolling a die, and you win if you roll a 6. Your odds of winning are 1 to 5 because there’s 1 favorable outcome (rolling a 6) and 5 unfavorable outcomes (rolling anything else). In logistic regression, we’re interested in these kinds of odds, but we usually talk about them in terms of log odds. The log odds is just the logarithm of the odds. Why do we use log odds? They have a nice property where they can take any value from negative infinity to positive infinity, which works great with our logistic function!

How Logistic Regression Estimates Probabilities

Let’s say we have a magical coin that can predict whether it’s going to rain tomorrow. You toss the coin, and if it lands on heads, it predicts rain, and if it lands on tails, it predicts sun. Now, this coin isn’t always correct, but it’s correct often enough that we can use it to make pretty good predictions.

In logistic regression, we have something like this magical coin, but instead of a coin, we have a mathematical equation. This equation uses the features of our data (like humidity, wind speed, etc., if we’re predicting the weather) to estimate the log odds of an outcome. Then it uses the logistic function to convert these log odds into probabilities.

Just like the magical coin, this equation isn’t always right, but by finding the best values for our equation (a process called training the model), we can make it as right as possible!


Mathematical Representation of Logistic Regression

Let’s now imagine we’re chefs. We have a magical recipe (our logistic regression model) and ingredients (the features). We take different quantities of the ingredients (the coefficients or weights), mix them all together (calculate a weighted sum), and apply some magic (the logistic function). The result? A delicious probability pie that tells us the likelihood of each outcome!

Here’s the recipe in detail:

logit(p) = b0 + b1x1 + b2x2 + … + bn*xn

This is our magical recipe, where p is the probability of the positive outcome, b0, b1, … bn are the quantities of the ingredients, and x1, x2, … xn are the ingredients themselves. The logit(p) represents the log odds, and if we apply the logistic function to this, we get the probability pie!

  1. Interpretation and Implications of the Logistic Regression Coefficients

Remember the quantities of ingredients in our recipe? If we change the quantities, our pie will taste different. Similarly, in logistic regression, the coefficients determine how each feature influences the prediction.

If a coefficient is positive, increasing that feature’s value increases the odds of a positive outcome. If it’s negative, increasing that feature’s value decreases the odds of a positive outcome. The magnitude of the coefficient tells us how strong this effect is: the bigger the coefficient, the stronger the effect.

For example, if we have a model predicting whether it’ll rain, and the coefficient for humidity is positive and large, that means high humidity significantly increases the chances of rain.

Remember, logistic regression, like any other machine learning model, doesn't understand the problem like a human would. It simply learns patterns from the data it's given. So if we want our model to make good predictions, it's important that we provide good, representative data and choose our features wisely.


Imagine you’re watching a soccer match. You have a favorite player, and you want to predict whether he’ll score a goal. You have some information like how many shots on goal he had, his general scoring rate, the strength of the opposing team, and so forth. You cannot predict for sure, but you can estimate probabilities – like there’s a 70% chance he’ll score. Logistic Regression helps you do just that, but with more complex and exciting scenarios!

  1. Logistic Regression: Imagine a magic wand that can help you make predictions, not of exact numbers, but whether something will happen or not. Will it rain tomorrow? Will your team win the next game? Logistic Regression is like that wand. It’s a statistical model used when the outcome you’re trying to predict can fall into two distinct categories. In technical terms, it’s used for binary classification problems.
  2. Logit Function: To understand logistic regression, we need to talk about the Logit Function. Think of it as the brain of our magic wand. This function helps us link our input variables with the probability of our outcome happening. For instance, it helps us understand how the number of practice hours affects the probability of our soccer player scoring.
  3. Maximum Likelihood Estimation: You can think of Maximum Likelihood Estimation (MLE) as the fuel that powers our magic wand. This fancy term essentially means finding the most likely (or best fitting) line that divides the two categories in our data. This line helps us make our predictions.
  4. Odds and Odds Ratios: These are the magic spells we use to interpret our model. Odds tell us how likely it is for something to happen, compared to it not happening. Odds Ratios compare the odds under two different conditions. For example, what are the odds of our player scoring when he’s well-rested compared to when he’s tired?
  5. Binary Classification: This is the magic trick our wand performs! Binary Classification means we’re classifying our data into two groups. Will our player score a goal (yes or no)? That’s a binary classification problem.


Let’s make a trip to the world of medical science. Imagine you’re a doctor. Your patients come to you with different symptoms and medical histories. Based on your knowledge and experience, you often predict whether a patient has a certain disease or not. Logistic Regression is your digital assistant in this case, helping you predict binary outcomes – whether a patient has a disease (Yes or 1) or not (No or 0).

Another fascinating example is in the sports world. Let’s say you’re a football coach. You need to decide which players should be in your starting lineup. Their past performance stats, physical fitness levels, the opposition team’s strategy, and even the weather on the match day can be predictors. The outcome is binary – a player either makes it to the lineup (Yes or 1) or doesn’t (No or 0).

Similarly, Logistic Regression is used in various fields, such as finance (predicting whether a customer will default on a loan or not), marketing (whether a customer will buy a product or not), HR (whether an employee will leave or stay), and many more.

For our journey, we’re going to use a medical example to understand Logistic Regression. Our goal is to predict whether a patient has diabetes based on several health factors.

Working of Logistic Regression


To embark on our medical adventure, we need our essential tool – the dataset. In our case, it’s a popular dataset known as the Pima Indians Diabetes Database, available in Python’s scikit-learn library.

This dataset is a record of health details of Pima Indian women aged 21 and above. It was collected by the National Institute of Diabetes and Digestive and Kidney Diseases with the aim of predicting a binary outcome: whether a patient has diabetes. The dataset includes the following eight features:

  1. Number of Pregnancies: This represents the number of times the individual has been pregnant.
  2. Glucose: This is the plasma glucose concentration in an oral glucose tolerance test.
  3. Blood Pressure: This is the diastolic blood pressure (mm Hg).
  4. Skin Thickness: This is the triceps skin fold thickness (mm).
  5. Insulin: This is the 2-Hour serum insulin (mu U/ml).
  6. BMI: Body mass index, calculated as weight in kg/(height in m)2.
  7. Diabetes Pedigree Function: This provides some data on diabetes history in relatives and the genetic relationship of those relatives to the patient.
  8. Age: Age in years.

The target variable, Outcome, tells us whether the individual developed diabetes (represented by 1) or not (represented by 0).

Each row in the dataset represents a patient. There are a total of 768 patients (rows) and 8 features (columns), plus the target column.

In the upcoming sections, we will prepare this dataset and use Logistic Regression to make our predictions. Remember, the objective is not just to create a model but also to interpret it in a way that can help us understand the relationships between the features and the target variable. This will allow us to gain valuable insights into the factors that contribute to the onset of diabetes.

For now, let’s gear up and prepare ourselves to dive deeper into the world of Logistic Regression!

VIII. Applying Logistic Regression

Before we dive in, let me introduce you to the basic tools we’ll be using. Think of these as the ingredients in a recipe. Our main ingredient is Python, a simple and versatile programming language. We’ll also be using a few ‘flavors’ of Python, which are libraries of pre-written code that help us perform complex tasks easily. The main ones we’ll use are:

  • pandas: for handling our data
  • numpy: for numerical operations
  • matplotlib and seaborn: for creating beautiful, informative graphs
  • scikit-learn: our main machine learning toolkit

First, we need to gather our ingredients. In coding terms, this means importing our libraries:

# Importing required libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, classification_report

# Loading the dataset
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
data = pd.read_csv(url, names=names)

# Preparing the data
X = data.drop('class', axis=1)
y = data['class']

# Splitting the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)

# Training the model
model = LogisticRegression(max_iter=200)
model.fit(X_train, y_train)

# Making predictions
predictions = model.predict(X_test)

# Displaying the confusion matrix
conf_mat = confusion_matrix(y_test, predictions)
sns.heatmap(conf_mat, annot=True, fmt='d', cmap=plt.cm.coolwarm)

# Generating a classification report
print(classification_report(y_test, predictions))

In this report, ‘precision’ tells us what proportion of patients that we diagnosed with diabetes actually had diabetes. ‘Recall’ tells us what proportion of patients that actually had diabetes were correctly diagnosed by us. ‘F1-score’ gives us a single score that balances precision and recall. Finally, ‘support’ is the number of cases that were in each class (diabetes and no diabetes).



Interpreting the results of Logistic Regression may feel a bit like reading an encrypted message at first, but don’t worry, we’re here to decode it together! We’ll use the ‘classification report’ from your Logistic Regression model as our decryption key.

Let’s start with the precision and recall values. Imagine you’re playing a game of darts. Precision is like how closely your darts are clustered, while recall is how close those darts are to the bullseye. So a model with high precision gives results that are very consistent, and a model with high recall hits the target more often. In our report, we have two precision values: 0.79 for class 0, and 0.76 for class 1. This means our model is fairly consistent in predicting both classes. For recall, we have 0.90 for class 0 and 0.56 for class 1. This suggests that our model is good at identifying class 0 but needs improvement in identifying class 1.

The F1 score combines precision and recalls into a single measure. It’s kind of like a team captain who takes into account all players’ skills to plan the best strategy. An F1 score close to 1 is like a top-tier captain, leading the team to victory most of the time. In our model, the F1-scores are 0.84 for class 0 and 0.65 for class 1, which suggests our model performs better for class 0.

Lastly, accuracy is the ratio of correct predictions to total predictions. It’s like a school report card summarizing your performance across all subjects. An accuracy of 0.78 indicates that our model made correct predictions for 78% of the data.

Now, let’s move on to the concept of thresholding. In Logistic Regression, we use a threshold to decide whether a predicted probability results in class 0 or 1. This threshold is usually set at 0.5. However, adjusting this threshold can help improve the model’s performance, depending on our objective. For instance, if we’re diagnosing a serious illness, we might lower the threshold to ensure more cases are correctly identified, even at the risk of more false positives.


You might wonder, if Logistic Regression works so well, why do we even need other techniques? Well, imagine you’re a chef. Depending on the dish you’re cooking, you might need to use a knife, a whisk, or a blender. Each tool has its strengths and is suited for different tasks. Similarly, in machine learning, we have a toolbox of techniques, each with its strengths and weaknesses.

Logistic Regression, for instance, is a great ‘knife’ in our toolbox. It’s simple, fast, and provides probabilities for outcomes. However, it assumes a linear decision boundary, which means it might struggle with more complex datasets where classes cannot be separated by a straight line.

This is where other techniques like Decision Trees, Random Forests, and Support Vector Machines come in. Decision Trees are like ‘whisks’, stirring up different features at each branch, which allows them to capture complex patterns. However, they can overfit the data, leading to poor performance on new data. Random Forests solve this by combining many Decision Trees, much like using many whisks together to mix ingredients more effectively.

Support Vector Machines, on the other hand, are like ‘blenders’. They can transform data into higher dimensions to find the best decision boundary, making them powerful for both linear and non-linear classification tasks. However, they can be slow and complex to tune.

In the end, the best technique depends on your data and objectives, much like choosing the right tool depends on the dish you’re cooking!


Imagine you’re a superhero who has an array of different abilities. Depending on the situation, some powers may be more useful than others, and some might even have drawbacks. Similarly, Logistic Regression, like any superhero or statistical model, has its strengths and weaknesses. Let’s uncover them!


  1. Easy to Understand: Logistic Regression is like the trusty old bicycle in your garage. It might not be the fanciest vehicle, but it’s reliable and gets you where you need to go. It’s simple and easy to implement, and the output is also easy to interpret. It gives a nice probability score that tells us how likely an event is to occur.
  2. No Need for Linear Relationship: Unlike your bicycle that requires a flat road (linear relationship) for a comfortable ride, Logistic Regression can handle curvy, uphill paths! It doesn’t require a linear relationship between the dependent and independent variables. It can handle nonlinear effects as it uses a non-linear function, the logistic function.
  3. Robust: Logistic Regression is like a superhero’s suit; it’s tough and robust! It’s less prone to overfitting and can handle a decent number of features without throwing a fit.
  4. Useful Output: Remember how a superhero can estimate the probability of danger? Similarly, Logistic Regression estimates the probability of the occurrence of an event, which can be quite useful!


  1. Requires Large Sample Size: Logistic Regression is like a superhero who needs a good breakfast to function well. It requires a large sample size to achieve high accuracy and stability.
  2. Sensitive to Feature Selection: Logistic Regression is a bit picky like a superhero who carefully chooses their battles. Irrelevant or highly correlated features can affect its performance, so careful feature selection is necessary.
  3. Not Fit for Complex Relationships: Sometimes, there can be complex problems that a superhero can’t solve alone. In the same way, Logistic Regression is not the best choice when there’s a complex relationship between the features and the target variable. It can struggle with capturing complex patterns or interactions between variables.
  4. Assumption of Linearity: Logistic Regression assumes a linear relationship between the logit of the response and the predictors like a superhero assuming the villain is always in the city center. But if the villain changes tactics, the superhero might be caught off guard! If this assumption is violated, logistic regression may not perform well.


And with that, we conclude our exciting journey into the world of Logistic Regression. Much like a comic book adventure, we’ve dived deep into this fascinating world, understanding the origins (definition and background), powers (how it works), and the highs and lows (advantages and disadvantages) of our hero, Logistic Regression.

Through this journey, we’ve discovered that Logistic Regression, despite its simplicity, is an effective tool in the world of machine learning for tackling classification problems, with its ability to handle binary outcomes with grace and robustness. But like any superhero, it has its kryptonite – it needs a large sample size and careful feature selection, and it might stumble when dealing with complex relationships.

Remember, just as a superhero is chosen based on the situation, the choice of machine learning model also depends on the task at hand. Therefore, understanding the strengths and weaknesses of each model, as we did with Logistic Regression today, can help you choose the right tool for your data science tasks.

Stay tuned as we continue our machine-learning adventure with our upcoming articles on Decision Trees, Random Forests, and Support Vector Machines, each a superhero in their own right, ready to tackle different data challenges. So, get your superhero capes (and data sets) ready for the next exciting installment!

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