Source: Pierian Data. In this project we will be working with a fake advertising data set, indicating whether or not a particular internet user clicked on an Advertisement. We will try to create a logistic regression model that will predict whether or not they will click on an ad based off the features of that user.
Logistic regression is a method for classification: the problem to indentify to which label or category some new prediction belongs to, such as email in spam, good lenders, etc.
The most popular model is the binary clasification, which means the prediction is YES/NO. This is modelized with the Sigmoid Function (SF) as a probability. The SFis the key to LR: convert a continuous number into 0 or 1.
– LR is a method for classification: What labels are assigned to certain prediction.
– Binary classification: convention is to have 2 classes: 0 and 1
– The result is usually a probability, so we can assign 0 or 1 if <0.5, or >0.5
After training the model with LT the way to evaluate it is with the Confussion Matrix.… Read more...
Let’s begin our understanding of implementing Logistic Regression in Python for classification. For this lecture we will be working with the Titanic Data Set from Kaggle. We’ll be trying to predict if a passenger died or not in the accident.
We’ll use a “semi-cleaned” version of the Titanic data set, if you use the data set hosted directly on Kaggle, you may need to do some additional cleaning not shown in this lecture notebook.
The book Learn Data Science with R covers minimal theory, practical examples, and projects. It starts with an explanation of the underlying concepts of data science, followed by implementing them in R language. Learn linear regression, logistic regression, random forests, and other machine learning algorithms. The hands-on projects provide a detailed step-by-step guide for analyzing and predicting data.
The book covers the following topics –
Statistics and Mathematics
Using the ONNX format for deploying trained Scikit-learn Lead Scoring predictive model into the .NET ecosystem
While being part of a team working on designing and developing a lead scoring system prototype, I faced the challenge of integrating machine learning models into the target environment built around the Microsoft .NET ecosystem. Technically, I implemented the lead scoring predictive model using the Scikit-learn machine learning built-in algorithm for regression, more precisely Logistic Regression. Considering the phases of initial data analysis, data preprocessing, exploratory data analysis (EDA), and the data preparation for the model building itself, I used the Jupyter Notebook environment powered by Anaconda distribution for Python scientific computing.
As we all know, data science as a discipline is very new to our world. This makes it a very exciting field in which to work. But it also creates problems. Today I want to talk about one of those problems which I deal with all the time: using the wrong language to describe data science results or concepts.
Here are five words that I commonly see misused, as well as an explanation of the typical misuses. Hopefully, this will help you become more aware of booby traps in the communication and implementation of data science results.
OMG, people LOVE the world predictive, don’t they? Since around 2010 when it started to come into fashion, I don’t think I have heard a word get bandied about like the p-word.… Read more...
Before we learn about the hyperparameter tuning methods, we should know what is the difference between hyperparameter and parameter.
The key difference between hyperparameter and parameter is where they are located relative to the model.
A model parameter is a configuration variable that is internal to the model and whose value can be estimated from data.
Example: coefficients in logistic regression/linear regression, weights in a neural network, support vectors in SVM
A model hyperparameter is a configuration that is external to the model and whose value cannot be estimated from data.
Example: max_depth in Decision Tree, learning rate in a neural network, C and sigma in SVM
Another important term that is also needed to be understood is the hyperparameter space.
Image classification is one of the hottest fields of machine learning, data science, and AI, and often used to benchmark certain types of AI algorithms — from logistic regression to deep neural networks.
But for now, I want to take your mind away from those hot techniques, and ask ourselves a question: if us humans saw an image of a handwritten character, or a dog or cat, how would our brains intuitively classify different types of images? Below is an example of digits in an image; “2”, “0”, “1” and “9”.
In the example above of digits (or numbers/numerals), how would our brains differentiate between, say, the 1 and 9 at the bottom? Well, intuitively our brains have a sort of “mental model” of what 1s look like, and a mental model of what 9s look like.
I’ve always been fascinated by Logistic Regression. It’s a fairly simple yet powerful Machine Learning model that can be applied to various use cases. It’s been widely explained and applied, and yet, I haven’t seen many correct and simple interpretations of the model itself. Let’s crack that now.
I won’t dive into the details of what Logistic Regression is, where it can be applied, how to measure the model error, etc. There’s already been lots of good writing about it. This post will specifically tackle the interpretation of its coefficients, in a simple, intuitive manner, without introducing unnecessary terminology.
Let’s first start from a Linear Regression model, to ensure we fully understand its coefficients.
We all have developed numerous regression models in our lives. But only few are familiar with using regression models for classification. So my intention is to reveal the beauty of this hidden world.
As we all know, when we want to predict a continuous dependent variable from a number of independent variables, we used linear/polynomial regression. But when it comes to classification, we can’t use that anymore.
Fundamentally, classification is about predicting a label and regression is about predicting a quantity.
Why linear regression can’t use for classification? The main reason for that is the predicted values are continuous, not probabilistic.
Is it statistics or ML? Wait, isn’t ML just advanced statistics? I have come across several versions of these questions in my 14 years career working with data. There are debates between high-profile experts, articles, and even peer-reviewed articles in prestigious journals on this topic. It’s crazy.
Honestly, this is a useless, (seemingly) inconclusive debate. ML is by definition concerned with learning from data. A key component of learning from data often requires transforming raw data into summary variables. A good chunk of statistics is all about summarising data. We now have an increasingly vast amount of data and require ingenious algorithmic approaches. A lot of these have been developed by the community sitting in computer science departments.
In this article, we’ll cover the fundamentals you need to know to use LASSO regression:
- We’ll briefly cover the theory behind LASSO.
- We’ll talk about why correct usage of LASSO requires features with similar scales.
- We’ll cover how to interpret the coefficients in Linear Regression and LASSO Regression with standardized features.
- We’ll introduce the dataset and give some insight into why LASSO helps.
- We’ll show how to implement Linear Regression, LASSO Regression and Ridge Regression in SciKit-Learn.
In a previous article, we discuss how and why LASSO increases the interpretability and accuracy of Generalized Linear Models. We’ll recap the basics here, but if you are interested in a deeper dive into the theory, have a look at the article below.
By Zachary Warnes, Data Scientist
This post is meant for new and or aspiring data scientists trying to decide what model to use for a problem.
This post will not be going over data wrangling. Which hopefully, you know, is the majority of the work a data scientist does. I’m assuming you have some data ready, and you want to see how you can make some predictions.
There are many models to choose from with seemingly endless variants.
There are usually only slight alterations needed to change a regression model into a classification model and vice versa. Luckily this work has already been done for you with the standard python supervised learning packages. So you only need to select what option you want.
When I first started to learn about data science, machine learning sounded like an extremely difficult subject. I was reading about algorithms with fancy names such as support vector machine, gradient boosted decision trees, logistic regression, and so on.
It did not take me long to realize that all those algorithms are essentially capturing the relationships among variables or the underlying structure within the data.
Some of the relationships are crystal clear. For instance, we all know that, everything else being equal, the price of a car decreases as it gets older (excluding the classics). However, some relationships are not so intuitive and not easy for us to notice.
Missing data sucks. It prevents the use of certain models and often requires complex judgement calls by the engineer. However, in 2021, researchers at the University of Auckland developed a solution…
Their method leverages the world-famous XGBoost algorithm to impute missing data. By relying on a model that’s optimized for speed, we can see 10–100x performance boosts relative to traditional imputation methods. XGBoost also requires little to no hyperparameter tuning which significantly reduces engineer workload. XGBoost is also able to maintain complex relationships observed in the data, such as interactions and non-linear relationships.
PS is a balancing score: conditional on the PS, the distribution of the observed covariates looks similar between treated and control groups (Austin, 2011). Thus, it allows you to adjust for the covariate imbalance by tweaking the score.
Some researchers argue that we can match participants based on their PS and find comparable cases, i.e., PS Matching. However, the precise matching process increases imbalance, inefficiency, model dependence, bias and fails to reduce the imbalance (King and Nielsen, 2019). In contrast, PS Stratification offers a better alternative to PS Matching.
Here are the specific steps:
1. Estimate the PS using a logistic regression
2. Create mutually exclusive strata based on the estimated PS
Excel is often poorly regarded as a platform for regression analysis. The regression add-in in its Analysis Toolpak has not changed since it was introduced in 1995, and it was a flawed design even back then. (See this link for a discussion.) That’s unfortunate, because an Excel file can be a very good place in which to build regression models, compare and refine them, create high-quality editable tables and charts, share and present the results, and teach regression to those constituencies of students and practitioners for whom Excel is the only analytic tool they may ever use on a regular basis.
Over the last 10 years I’ve developed an alternative, a free add-in called RegressIt, which is designed to take maximal advantage of the Excel environment and support good practices of data analysis.
I have included a lot of Excel spreadsheets in the numerous articles and books that I have written in the last 10 years, based either on real life problems or simulations to test algorithms, and featuring various machine learning techniques. It is time to create a new blog series focusing on these useful techniques that can easily be handled with Excel. Data scientists typically use programming languages and other visual tools for these techniques, mostly because they are unaware that it can be accomplished with Excel alone. This article is my first one in this new series. The series will appeal to BI analysts, managers presenting insights to decision makers, as well as software engineers or MBA people who do not have a strong data science background.
By Venkat Raman, Co-Founder Aryma Labs.
Image source: Unsplash
Binary Logistic Regression is used as a Classification algorithm when we want the response variable to be dichotomous (Churn/Not Churned, Pass/Fail, Spam/No spam etc.)
Usually, we make Logistic Regression into a classification algorithm by setting an appropriate probability cut-off or threshold (0.4, 0.5, 0.6 etc.).
The problem of classifying using a threshold value
Fixing the probability threshold is purely a business call and not a statistical one.
Frank Harrell, in his blog1 aptly, makes the point “classification is a forced choice”.
Now consider this example. You choose a threshold value of 0.5. The ML algorithm outputs the probability of default or no default (1- default, 0 — no default) for 4 customers as 0.51, 0.49, 0.23 and 0.92.