Illustration Photo by Oleg Magni from PexelsMachine LearningThis post presents solving Tabular primary data via the two most common Machine Learning (ML) tasks — classification and regression, with Lightning Flash, which makes it very simple.When it comes to articles on deep learning, advances in Computer Vision or Natural Language Processing (NLP) receive the lion's share of the attention. Advancement in CV and NLP is fantastic and super exciting; however, many data scientists' day-to-day tasks revolve around tabular data processing.Tabular data classification and regression are…
Let’s implement Linear Regression from scratch…….Continue reading on Towards AI »
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.
Machine learning is more then “.fit” and “.predict”…Continue reading on Towards AI »
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.
CareersI have long been asking myself, why data science is one of the hottest jobs of our century? I found the answer to this question while discussing linear regression with a Ph.D. researcher in Chemistry who is conducting research to develop a bio-plastic. So the answer lays in the scalability of the tools and techniques used by statisticians. In our case, I will take an example the linear regression and its power!!!I would reformulate the title of the article as “What Chemistry has in common with Finance?”. The answer is data! Each of the sectors has the data to study and the data…
In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.
Practical definition in ML
Given a dataset, we want to predict a range of numeric (continuous) values. One or several variables of the dataset predict (are correlated with) a numerical outcome (the future), which is usually another column in the data.… Read more...
For the last 200-300 years there´s something called regression statistics, a regression algorithm which relates known, pre-defined things (today is friday) to know about other things (you are using LinkedIn).
But with Machine Learning we are getting into Bayesian Algorithms, where you don´t need a human to pre-define whats important.
Instead, the computer looks at tons of variables and find hidden correlations. The result is that you can really find all the little details that somehow add up and contribute for someone to open and use LinkedIn today.
In my opinion, the most interesting intellectual challente right now is not in the algorithms themselves, but in finding situations where algorithms can really make a difference optimizing and adding value to that activity or industry.… Read more...
Scikit-learn is the most popular open-source and free python machine learning library for Data scientists and Machine learning practitioners. The scikit-learn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction.Read the full story
Lets review a classical model from Pierian Data. Your neighbor is a real estate agent and wants some help predicting housing prices for regions in the USA. It would be great if you could somehow create a model with Python and scikit-learn for her, that allows her to put in a few features of a house and returns back an estimate of what the house would sell for.
She has asked you if you could help her out with your new data science skills. You say yes, and decide that Linear Regression might be a good path to solve this problem!.
Your neighbor then gives you some information about a bunch of houses in regions of the United States,it is all in the data set: USA_Housing.csv.
The data contains the following columns:
‘Avg. Area Income’: Avg.… Read more...
Everybody knows what Gradient Descent is and how it works. Ever wondered why it works? Here’s a mathematical explanationPhoto by Yuriy Chemerys on UnsplashWhat is Gradient Descent?Gradient descent is an iterative optimization algorithm that is used to optimize the weights of a machine learning model (linear regression, neural networks, etc.) by minimizing the cost function of that model.The intuition behind gradient descent is this: Picture the cost function (denoted by f(Θ̅ ) where…
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
Luckily, the lazy habit of writing “bug fixes and stability improvements” hasn’t found its way to the software libraries’ release notes . Without checking these notes, I wouldn’t have realized that Scikit-Lean version 0.23 implements Generalized Linear Models (GLM).
I pay extra attention to Scikit-Learn. Not only because I use it all the time, but also, after publishing my book, Hands-On Machine Learning with Scikit-learn and Scientific Python Toolkits, I want to keep track of the library’s newly implemented algorithms and features to write about them here as a pseudo-appendix to my book.
As its name suggests, the Generalized Linear Model is an extension to our ultimate favorite Linear Regression algorithm.… Read more...
Recently, I wrote an article explaining the utilization of the ONNX format in integrating the Scikit-learn lead scoring machine learning model into the .NET ecosystem. I described one possible way of deploying the Python-based regression model as Microsoft Azure Function. That is a procedure applicable for integrating the trained model as part of the Web API or Console Application as well. What I have mentioned there was the opportunity to use the approach for bridging the technical differences between the different data science and application development platforms, in this case targeting the .NET… Read more...
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...
By Lekshmi S. Sunil, IIT Indore ’23 | GHC ’21 Scholar.
Statistical analysis allows us to derive valuable insights from the data at hand. A sound grasp of the important statistical concepts and techniques is absolutely essential to analyze the data using various tools.
Before we go into the details, let’s take a look at the topics covered in this article:
- Descriptive vs. Inferential Statistics
- Data Types
- Probability & Bayes’ Theorem
- Measures of Central Tendency
- Measures of Dispersion
- Probability Distributions
- Hypothesis Testing
Descriptive vs. Inferential Statistics
Statistics as a whole deals with the collection, organization, analysis, interpretation, and presentation of data.
Regression analysis is one of the methods supplied “built-in” with SAP BW Data Mining. Based on this method regression models can be created and configured to satisfy specific analysis requirements (e.g., choice between linear or non-linear approximation, etc.). The method includes regression-specific reporting that allows analysis of the modeling results. In this paper we are suggesting a number of ways to extend this reporting in order to improve insight into the results of…
Continue reading: http://www.datasciencecentral.com/xn/detail/6448529:BlogPost:1070388
Linear Regression is usually the first algorithm that people learn for Machine Learning and Data Science. Linear Regression is a linear model that assumes a linear relationship between the input variables (
X) and the single output variable (
y). In general, there are two cases:
- Single Variable Linear Regression: it models the relationship between a single input variable (single feature variable) and a single output variable.
- Multi-Variable Linear Regression (also known as Multivariate Linear Regression): it models the relationship between multiple input variables (multiple features variables) and a single output variable.
This algorithm is common enough that Scikit-learn has this functionality built-in with
Many popular machine learning libraries use the concept of hyperparameters. These can be though of as configuration settings or controls for your machine learning model. While many parameters are learned or solved for during the fitting of your model (think regression coefficients), some inputs require a data scientist to specify values up front. These are the hyperparameters which are then used to build and train the model.
One example in gradient boosted decision trees is the depth of a decision tree. Higher values yield potentially more complex trees that can pick up on certain relationships, while smaller trees may be able to generalize better and avoid overfitting to our outcome — potentially leading to issues when predicting unseen data.
What is Regression Testing?
Regression testing is a process of testing the software and analyzing whether the change of code, update, or improvements of the application has not affected the software’s existing functionality.
Regression testing in software engineering ensures the overall stability and functionality of existing features of the software. Regression testing ensures that the overall system stays sustainable under continuous improvements whenever new features are added to the code to update the software.
Regression testing helps target and reduce the risk of code dependencies, defects, and malfunction, so the previously developed and tested code stays operational after the modification.
Generally, the software undergoes many tests before the new changes integrate into the main development branch of the code.
Orthogonality is a mathematical property that is beneficial for statistical models. It’s particularly helpful when performing factorial analysis of designed experiments.
Orthogonality has various mathematic and geometric definitions. In this post, I’ll define it mathematically and then explain its practical benefits for statistical models.
First, here’s a bit of background terminology that you’ll encounter when discussing orthogonality.
In math, a matrix is a two-dimensional rectangular array of numbers with columns and rows. A vector is simply a matrix that has either one row or one column.
For a regression model, the columns in your dataset are the independent and dependent variables. These columns are vectors.
“Thus learning is not possible without inductive bias, and now the question is how to c right bias. This is called model selection.” ETHEN ALPAYDIN (2004) p33 (Introduction to Machine Learning)
Really there are many more definitions concerning Model Selection. In this article, we are going to discuss Model Selection and its strategy for Data Scientists and Machine Learning Engineers.
An ML model(s) are always constructed using various mathematical frameworks and that would generate predictions based on the nature of the dataset and finding patterns out of it.
Most of them are really confused between two terminologies in machine learning – ML-Model and ML-Algorithm. Even me too. But over the period I got to understand the thin line between these two terms.… Read more...
Scikit-learn is a popular Python package among the data science community, as it offers the implementation of various classification, regression, and clustering algorithms. One can train a classification or regression machine learning model in few lines of Python code using the scikit-learn package.
Pandas is another popular Python library that offers to handle and preprocessing data prior to feeding it to a scikit-learn model. One can easily process and train an in-memory dataset (data that can fit into the RAM memory) using Pandas and Scikit-learn packages, but when it comes to working with a large dataset or out-of-memory dataset (data that cannot fit into the RAM memory), it fails, and cause memory issue.