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How To Prepare Your Data for Your Machine Learning Model

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Exploratory data analysis, or EDA for short, is exactly what it sounds like, exploring your data. In the real world, datasets are not as clean or intuitive as Kaggle datasets. The more you explore and understand the data you're working with, the easier it'll be when it comes to data preprocessing. Determine what the feature (input) variables are and what the target variable is. Don't worry about determining what the final input variables are, but make sure you can identify both types of variables.


Score streaming data with a machine learning model

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This is part of the Learning path: Get started with IBM Streams. In this developer code pattern, we will be streaming online shopping data and using the data to track the products that each customer has added to the cart. We will build a k-means clustering model with scikit-learn to group customers according to the contents of their shopping carts. The cluster assignment can be used to predict additional products to recommend. Our application will be built using IBM Streams on IBM Cloud Pak for Data.


Using Orange to Build a Machine Learning Model

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Orange is an open-source, GUI based platform that is popularly used for rule mining and easy data analysis. The reason behind the popularity of this platform is it is completely code-free. Researchers, students, non-developers and business analysts use platforms like Orange to get a good understanding of the data at hand and also quickly build machine learning models to understand the relationship between the data points better. Orange is a platform built on Python that lets you do everything required to build machine learning models without code. Orange includes a wide range of data visualisation, exploration, preprocessing and modelling techniques. Not only does it become handy in machine learning, but it is also very useful for associative rule mining of numbers, text and even network analysis.


Building A Machine Learning Model With WEKA With 'No Coding'

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No-code environments in machine learning have become increasingly popular due to the fact that almost anybody who needs machine learning, whatever field they may be in, can use these tools to build models for themselves. WEKA is one of the early no-code tools that was developed but is very efficient and powerful. WEKA can be used to implement state of the art machine learning and deep learning models and can support numerous file formats. In this article, we will learn about how to use WEKA to pre-process and build a machine learning model with code. WEKA can be used in Linux, Windows or Mac operating systems and you can download this from the official website here.


ROC-AUC Curve For Comprehensive Analysis Of Machine Learning Models

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In machine learning when we build a model for classification tasks we do not build only a single model. We never rely on a single model since we have many different algorithms in machine learning that work differently on different datasets. We always have to build a model that best suits the respective data set so we try building different models and at last we choose the best performing model. For doing this comparison we cannot always rely on a metric like an accuracy score, the reason being for any imbalance data set the model will always predict the majority class. But it becomes important to check whether the positive class is predicted as the positive and negative class as negative by the model.


Machine learning models predict how much time aging mice have left - ScienceBlog.com

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How old are you for your age? Scientists who study aging have begun to distinguish chronological age: how long it's been since a person was born, and so-called biological age: how much a body is "aged" and how close it is to the end of life. These researchers are uncovering ways to measure biological age, from grip strength to the lengths of protective caps on the ends of chromosomes, known as telomeres. Their goal: to construct a comprehensive set of metrics that predicts an individual's life span and health span -- the number of healthy years they have left -- and illuminates the drivers of, and treatments for, age-related diseases. A team led by David Sinclair, professor of genetics in the Blavatnik Institute at Harvard Medical School, has just taken another step toward this goal by developing two artificial intelligence-based clocks that use established measures of frailty to gauge both chronological and biological age in mice.


Explainable and Reproducible Machine Learning Model Development with DALEX and Neptune - KDnuggets

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By Jakub Czakon, Sr Data Scientist at neptune.ai, Machine learning model development is hard, especially in the real world. And that is not all. You should have the experiments you run and models you train versioned in case you or anyone else needs to inspect them or reproduce the results in the future. From my experience, this moment comes when you least expect it and the feeling of "I wish I had thought about it before" is so very real (and painful).


Build No-code Automated Machine Learning Model with OptimalFlow Web App

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In the latest version(0.1.10) of OptimalFlow, it added a Flask-based'no-code' Web App as a GUI. Users could build Automated Machine Learning Models all by clicks, without any coding (Documentation). OptimalFlow was designed highly modularized at the beginning, which made it easy to continue developing. And users could build applications based on it. The web app of OptimalFlow is a user-friendly tool for people who don't have coding experience to build an Omni-ensemble Automated Machine Learning workflow simply and quickly.


Explaining Your Machine Learning Models with SHAP and LIME!

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Welcome back again to another data science quick tip. This particular post is most interesting for me not only because this is the most complex subject we've tackled to date, but it's also one that I just spent the last few hours learning myself. And of course, what better way to learn than to figure out how to teach it to the masses? Before getting into it, I've uploaded all the work shown in this post to a singular Jupyter notebook. You can find it at my personal GitHub if you'd like to follow along more closely.


The Insiders' Guide to Generative and Discriminative Machine Learning Models - KDnuggets

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In this article, we will look at the difference between generative and discriminative models, how they contrast, and one another. Discriminative machine learning is to recognize the rig output among possible output choices. Given something about the data, and done by learning parameters. Classification is additionally mentioned as discriminative modeling. This is often on the grounds; the model must separate instances of input variables across classes.