Make kNN 300 times faster than Scikit-learn’s in 20 lines!

Introduction

k Nearest Neighbors (kNN) is a simple ML algorithm for classification and regression. Scikit-learn features both versions with a very simple API, making it popular in machine learning courses. There is one issue with it — it’s quite slow! But don’t worry, we can make it work for bigger datasets with the Facebook faiss library.

The kNN algorithm has to find the nearest neighbors in the training set for the sample being classified. As the dimensionality (number of features) of the data increases, the time needed to find nearest neighbors rises very quickly. To speed up prediction, in the training phase (.fit() method) kNN classifiers create data structures to keep the training dataset in a more organized way, that will help with nearest neighbor searches.

#algorithms #knn #machine-learning #data-science #k-nearest-neighbours

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Make kNN 300 times faster than Scikit-learn’s in 20 lines!

Make kNN 300 times faster than Scikit-learn’s in 20 lines!

Introduction

k Nearest Neighbors (kNN) is a simple ML algorithm for classification and regression. Scikit-learn features both versions with a very simple API, making it popular in machine learning courses. There is one issue with it — it’s quite slow! But don’t worry, we can make it work for bigger datasets with the Facebook faiss library.

The kNN algorithm has to find the nearest neighbors in the training set for the sample being classified. As the dimensionality (number of features) of the data increases, the time needed to find nearest neighbors rises very quickly. To speed up prediction, in the training phase (.fit() method) kNN classifiers create data structures to keep the training dataset in a more organized way, that will help with nearest neighbor searches.

#algorithms #knn #machine-learning #data-science #k-nearest-neighbours

Michael  Hamill

Michael Hamill

1618278600

Scikit-Learn Is Still Rocking, Been Introduced To French President

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

Vaughn  Sauer

Vaughn Sauer

1622792520

Top Free Resources To Learn Scikit-Learn

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.

Scikit-Learn Tutorials

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.

Perform Sentiment Analysis with Scikit-Learn

**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.

Python Machine Learning: Scikit-Learn Tutorial

**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.

Scikit Learn Tutorial | Machine Learning with Python

**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.

Regression using Scikit-Learn

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.

Machine Learning with Scikit-Learn Tutorial

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.

Predict Sales Revenue with Scikit-Learn

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.

SciPy 2016 Scikit-learn Tutorial

**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.

Build NLP pipelines using Scikit-Learn

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

SangKil Park

1597810812

5x Faster Scikit-Learn Parameter Tuning in 5 Lines of Code

Everyone knows about Scikit-Learn — it’s a staple for data scientists, offering dozens of easy-to-use machine learning algorithms. It also provides two out-of-the-box techniques to address hyperparameter tuning: Grid Search (GridSearchCV) and Random Search (RandomizedSearchCV).

Though effective, both techniques are brute-force approaches to finding the right hyperparameter configurations, which is an expensive and time-consuming process!

#data-science #machine-learning #python #deep-learning #scikit-learn

Alec  Nikolaus

Alec Nikolaus

1599472523

Using Scikit-learn’s Binary Trees to Efficiently Find Latitude and Longitude Neighbors

Bridging together sets of GPS coordinates without breaking your Python interpreter

Image for post

Image by Mohamed Hassan from Pixabay

Engineering features from latitude and longitude data can seem like a messy task that may tempt novices into creating their own apply function (or even worse: an enormous for loop). However, these types of brute force approaches are potential pitfalls that will unravel quickly when the size of the dataset increases.

For example: Imagine you have a single dataset of n items. The time it takes to explicitly compare these n items against n-1 other items essentially approaches . Meaning that with each doubling of rows in your dataset, the time it takes to find all nearest neighbors will increase by a factor of 4!

Fortunately, you do not need to calculate the distance between every point. There are a few data structures to efficiently determine neighbors right in scikit-learn that leverage the power of priority queues.

They can be found within the neighbors module and this guide will show you how to use two of these incredible classes to tackle this problem with ease.

Getting started

To begin we load the libraries.

import numpy as np
from sklearn.neighbors import BallTree, KDTree

## This guide uses Pandas for increased clarity, but these processes
## can be done just as easily using only scikit-learn and NumPy.
import pandas as pd

Then we’ll make two sample DataFrames based on weather station locations that are publicly available from the National Oceanic and Atmospheric Administration.

#machine-learning #data-science #python #scikit-learn #knn