Model selection | Dimension reduction
In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection techniques are used for several reasons: * simplification of models to make them easier to interpret by researchers/users, * shorter training times, * to avoid the curse of dimensionality, * improve data's compatibility with a learning model class, * encode inherent symmetries present in the input space. The central premise when using a feature selection technique is that the data contains some features that are either redundant or irrelevant, and can thus be removed without incurring much loss of information. Redundant and irrelevant are two distinct notions, since one relevant feature may be redundant in the presence of another relevant feature with which it is strongly correlated. Feature selection techniques should be distinguished from feature extraction. Feature extraction creates new features from functions of the original features, whereas feature selection returns a subset of the features. Feature selection techniques are often used in domains where there are many features and comparatively few samples (or data points). Archetypal cases for the application of feature selection include the analysis of written texts and DNA microarray data, where there are many thousands of features, and a few tens to hundreds of samples. (Wikipedia).
Recommender Systems - Feature Generation - Session 14
Features: local, user, item, global PageRank: graph features
From playlist Recommenders Systems (Hands-on)
From playlist Data Science Course
Feature Selection for Scikit Learn
We learn about several feature selection techniques in scikit learn including: removing low variance features, score based univariate feature selection, recursive feature elimination, and model based feature selection Associated Github Commit: https://github.com/knathanieltucker/bit-of-da
From playlist A Bit of Data Science and Scikit Learn
Feature Engineering | Applied Machine Learning, Part 1
Explore how to perform feature engineering, a technique for transforming raw data into features that are suitable for a machine learning algorithm. Feature engineering starts with your best guess about what features might influence the action you’re trying to predict. After that, it’s an
From playlist Applied Machine Learning
Analyze the characteristics of multiple functions
👉 Learn about the characteristics of a function. Given a function, we can determine the characteristics of the function's graph. We can determine the end behavior of the graph of the function (rises or falls left and rises or falls right). We can determine the number of zeros of the functi
From playlist Characteristics of Functions
Feature selection in Machine Learning | Feature Selection Techniques with Examples | Edureka
🔥Edureka Data Scientist Course Master Program https://www.edureka.co/masters-program/data-scientist-certification (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎") This Edureka tutorial explains the 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠, Various techniques used for feature selection like filter methods, wrapper me
From playlist Data Science Training Videos
Applied ML 2020 - 12 - AutoML (plus some feature selection)
The second part of the feature selection lecture, plus an overview of automl approaches. Sorry for the chat window, I didn't realize that was recorded as well. I'll see if I can change that in the future.
From playlist Applied Machine Learning 2020
Feature Selection In Machine Learning | Feature Selection Techniques With Examples | Simplilearn
🔥Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=FeatureSelectioninMachineLearning&utm_medium=Descriptionff&utm_source=youtube 🔥Professional Certificate Program In AI And Machine Learning: https
Dimensionality Reduction: From Kaggle to Real-World | by Carlos Huertas | Kaggle Days San Francisco
Carlos Huertas "Dimensionality Reduction: From Kaggle to Real-World" Kaggle Days San Francisco held in April 2019 gathered over 300 participants to meet, learn and code with Kaggle Grandmasters, and compete in our traditional offline competition. This edition is presented by LogicAI wi
From playlist Kaggle Days San Francisco Edition | by LogicAI + Kaggle
How do I select features for Machine Learning?
Selecting the "best" features for your Machine Learning model will result in a better performing, easier to understand, and faster running model. But how do you know which features to select? In this video, I'll discuss 7 feature selection tactics used by the pros that you can apply to yo
From playlist Insider Insights
Applied Machine Learning 2019 - Lecture 12 - Model Interpretration and Feature Selection
Feature importance measures, partial dependence plots. Univariate and multivariate feature selection, recursive feature selection. Slides and more materials are on the class website: https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/
From playlist Applied Machine Learning - Spring 2019
Applied ML 2020 - 11 - Model Inspection and Feature Selection
Course materials at https://www.cs.columbia.edu/~amueller/comsw4995s20/schedule/
From playlist Applied Machine Learning 2020
Add feature selection to a Pipeline
It's simple to add feature selection to a Pipeline: 1. Use SelectPercentile to keep the highest scoring features 2. Add feature selection after preprocessing but before model building P.S. Make sure to tune the percentile value! 👉 New tips every TUESDAY and THURSDAY! 👈 🎥 Watch all tips:
From playlist scikit-learn tips
👉 Learn about the characteristics of a function. Given a function, we can determine the characteristics of the function's graph. We can determine the end behavior of the graph of the function (rises or falls left and rises or falls right). We can determine the number of zeros of the functi
From playlist Characteristics of Functions
DSI | Simultaneous Feature Selection and Outlier Detection Using Mixed-Integer Programming
Simultaneous Feature Selection and Outlier Detection Using Mixed-Integer Programming with Optimality Guarantees Biomedical research is increasingly data rich, with studies comprising ever growing numbers of features. The larger a study, the higher the likelihood that a substantial portion
From playlist DSI Virtual Seminar Series
👉 Learn about the characteristics of a function. Given a function, we can determine the characteristics of the function's graph. We can determine the end behavior of the graph of the function (rises or falls left and rises or falls right). We can determine the number of zeros of the functi
From playlist Characteristics of Functions
👉 Learn about the characteristics of a function. Given a function, we can determine the characteristics of the function's graph. We can determine the end behavior of the graph of the function (rises or falls left and rises or falls right). We can determine the number of zeros of the functi
From playlist Characteristics of Functions