1603198800

Feature selection is an important task for any machine learning application. This is especially crucial when the data in question has many features. The optimal number of features also leads to improved model accuracy. Obtaining the most important features and the number of optimal features can be obtained via feature importance or feature ranking. In this piece, we’ll explore feature ranking.

The first item needed for recursive feature elimination is an estimator; for example, a linear model or a decision tree model.

These models have coefficients for linear models and feature importances in decision tree models. In selecting the optimal number of features, the estimator is trained and the features are selected via the coefficients, or via the feature importances. The least important features are removed. This process is repeated recursively until the optimal number of features is obtained.

Scikit-learn makes it possible to implement recursive feature elimination via the `sklearn.feature_selection.**RFE**`

class. The class takes the following parameters:

`estimator`

— a machine learning estimator that can provide features importances via the`coef_`

or`feature_importances_`

attributes.`n_features_to_select`

— the number of features to select. Selects`half`

if it’s not specified.`step`

— an integer that indicates the number of features to be removed at each iteration, or a number between 0 and 1 to indicate the percentage of features to remove at each iteration.

Once fitted, the following attributes can be obtained:

`ranking_`

— the ranking of the features.`n_features_`

— the number of features that have been selected.`support_`

— an array that indicates whether or not a feature was selected.

#overviews #feature selection #machine learning #python #scikit-learn

1603198800

Feature selection is an important task for any machine learning application. This is especially crucial when the data in question has many features. The optimal number of features also leads to improved model accuracy. Obtaining the most important features and the number of optimal features can be obtained via feature importance or feature ranking. In this piece, we’ll explore feature ranking.

The first item needed for recursive feature elimination is an estimator; for example, a linear model or a decision tree model.

These models have coefficients for linear models and feature importances in decision tree models. In selecting the optimal number of features, the estimator is trained and the features are selected via the coefficients, or via the feature importances. The least important features are removed. This process is repeated recursively until the optimal number of features is obtained.

Scikit-learn makes it possible to implement recursive feature elimination via the `sklearn.feature_selection.**RFE**`

class. The class takes the following parameters:

`estimator`

— a machine learning estimator that can provide features importances via the`coef_`

or`feature_importances_`

attributes.`n_features_to_select`

— the number of features to select. Selects`half`

if it’s not specified.`step`

— an integer that indicates the number of features to be removed at each iteration, or a number between 0 and 1 to indicate the percentage of features to remove at each iteration.

Once fitted, the following attributes can be obtained:

`ranking_`

— the ranking of the features.`n_features_`

— the number of features that have been selected.`support_`

— an array that indicates whether or not a feature was selected.

#overviews #feature selection #machine learning #python #scikit-learn

1618278600

Amilestone for open source projects — French President Emmanuel Macron has recently been introduced to Scikit-learn. In fact, in a recent tweet, Scikit-learn creator and Inria tenured research director, Gael Varoquaux announced the presentation of Scikit-Learn, with applications of machine learning in digital health, to the president of France.

He stated the advancement of this free software machine learning library — “started from the grassroots, built by a community, we are powering digital revolutions, adding transparency and independence.”

#news #application of scikit learn for machine learning #applications of scikit learn for digital health #scikit learn #scikit learn introduced to french president

1622792520

Scikit-Learn is one of the popular software machine learning libraries. The library is built on top of NumPy, SciPy, and Matplotlib and supports supervised and unsupervised learning as well as provides various tools for model fitting, data preprocessing, model selection and evaluation.

**About:** From the developers of Scikit-Learn, this tutorial provides an introduction to machine learning with Scikit-Learn. It includes topics such as problem setting, loading an example dataset, learning and predicting. The tutorial is suitable for both beginners and advanced students.

**About: **In this project-based course, you will learn the fundamentals of sentiment analysis, and build a logistic regression model to classify movie reviews as either positive or negative. You will learn how to develop and employ a logistic regression classifier using Scikit-Learn, perform feature extraction with The Natural Language Toolkit (NLTK), tune model hyperparameters and evaluate model accuracy etc.

**About: **Python Machine Learning: Scikit-Learn tutorial will help you learn the basics of Python machine learning. You will learn how to use Python and its libraries to explore your data with the help of Matplotlib and Principal Component Analysis (PCA). You will also learn how to work with the KMeans algorithm to construct an unsupervised model, fit this model to your data, predict values, and validate the model.

**About: **Edureka’s video tutorial introduces machine learning in Python. It will take you through regression and clustering techniques along with a demo of SVM classification on the famous iris dataset. This video helps you to learn the introduction to Scikit-learn and how to install it, understand how machine learning works, among other things.

**About:** In this Coursera offering, you will learn about Linear Regression, Regression using Random Forest Algorithm, Regression using Support Vector Machine Algorithm. Scikit-Learn provides a comprehensive array of tools for building regression models.

**About:** In this course, you will learn about machine learning, algorithms, and how Scikit-Learn makes it all so easy. You will get to know the machine learning approach, jargons to understand a dataset, features of supervised and unsupervised learning models, algorithms such as regression, classification, clustering, and dimensionality reduction.

**About:** In this two-hour long project-based course, you will build and evaluate a simple linear regression model using Python. You will employ the Scikit-Learn module for calculating the linear regression while using pandas for data management and seaborn for plotting. By the end of this course, you will be able to build a simple linear regression model in Python with Scikit-Learn, employ Exploratory Data Analysis (EDA) to small data sets with seaborn and pandas.

**About: **This tutorial is available on GitHub. It includes an introduction to machine learning with sample applications, data formats, preparation and representation, supervised learning: training and test data, the Scikit-Learn estimator interface and more.

**About:** This is a two-hour long project-based course, where you will understand the business problem and the dataset and learn how to generate a hypothesis to create new features based on existing data. You will learn to perform text pre-processing and create custom transformers to generate new features. You will also learn to implement an NLP pipeline, create custom transformers and build a text classification model.

#developers corner #learn scikit-learn #machine learning library #scikit learn

1617763740

One of the most crucial preprocessing steps in any machine learning project is feature encoding. It is the process of turning categorical data in a dataset into numerical data. It is essential that we perform feature encoding because most machine learning models can only interpret numerical data and not data in text form.

In this article, we will learn:

- The difference between a nominal variable and an ordinal variable
- How OneHotEncoder and OrdinalEncoder can be used to encode these variables respectively
- Why the Scikit-learn library is preferred over the Pandas library when it comes to encoding categorical features

As usual, I will demonstrate these concepts through a practical case study using the students’ performance in exams dataset on Kaggle.

You can find the complete notebook on my GitHub here.

#machine-learning #scikit-learn #feature-encoding #data-preprocessing #data-science

1618280760

Undoubtedly, Scikit-learn is one of the best machine learning libraries available today. There are several reasons for that. The consistency among Scikit-learn estimators is one reason. You cannot find such consistency in any other machine learning library. The .fit()/.predict() paradigm best describes the consistency. Another reason is that Scikit-learn has a variety of uses. It can be used for classification, regression, clustering, dimensionality reduction, anomaly detection.

Therefore, Scikit-learn is a must-have Python library in your data science toolkit. But, learning to use Scikit-learn is not straightforward. It’s not simple as you imagine. You have to set up some background before learning it. Even while you learning Scikit-learn, you should follow some guidelines and best practices. In this article, I’m happy to share 9 guidelines that worked for me to master the Scikit-learn without giving up the learning process in the middle. Whenever possible, I will include the links to my previous posts which will help you to set up the background and continue to learn the Scikit-learn.

#data-science #scikit-learn #machine-learning #unsupervised-learning #supervised-learning