ML-From-Scratch | Machine Learning From Scratch | Machine Learning library

by eriklindernoren Python Version: Current License: MIT

X-Ray Key Features Code Snippets(1)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | ML-From-Scratch Summary

ML-From-Scratch is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Example Codes applications. ML-From-Scratch has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install ML-From-Scratch' or download it from GitHub, PyPI.

Python implementations of some of the fundamental Machine Learning models and algorithms from scratch. The purpose of this project is not to produce as optimized and computationally efficient algorithms as possible but rather to present the inner workings of them in a transparent and accessible way.

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ML-From-Scratch has a medium active ecosystem.

It has 22189 star(s) with 4379 fork(s). There are 940 watchers for this library.

It had no major release in the last 6 months.

There are 32 open issues and 36 have been closed. On average issues are closed in 38 days. There are 18 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of ML-From-Scratch is current.

Quality

ML-From-Scratch has 0 bugs and 0 code smells.

Security

ML-From-Scratch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

ML-From-Scratch code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ML-From-Scratch is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

ML-From-Scratch releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed ML-From-Scratch and discovered the below as its top functions. This is intended to give you an instant insight into ML-From-Scratch implemented functionality, and help decide if they suit your requirements.

Train the discriminator
Generate images for the MNIST dataset
Set trainable value
Train the loss function
Calculate the cost for each sample
Compute the clusters for the given medoids
Calculates the cost of each sample
Compute the euclidean distance between two points
Backward of the layer
Build the discriminator layer
Predict cluster labels
Train the autoencoder
Find the most frequent items in the list
K - fold cross validation sets
Fit the network to the given data
Fit the model to the given data
Build the generator
Compute the DecisionStump
Fit the quadratic model
Train the model
Evolves the population
Run the crossover algorithm
Plots the class distribution in 2d
Evolve the population
Fit the model
Compute the posterior distribution

Get all kandi verified functions for this library.

ML-From-Scratch Key Features

No Key Features are available at this moment for ML-From-Scratch.

ML-From-Scratch Examples and Code Snippets

Machine Learning Algorithms - Simple and Naive Implementation

Python

Lines of Code : 51

License : Permissive (MIT)

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import numpy as np
from sklearn.datasets import load_boston

# from sklearn.linear_model import LinearRegression
# from sklearn.model_selection import train_test_split
# from sklearn.metrics import mean_absolute_error
# from sklearn.preprocessing imp

Community Discussions

Trending Discussions on ML-From-Scratch

Why the predicted value by LinearRegression is exactly the same as the true value?

QUESTION

Why the predicted value by LinearRegression is exactly the same as the true value?

Asked 2020-Sep-24 at 23:10

I'm doing a regression by LinearRegression and get the mean squared error 0. I think there should be some deviation(at least small). Could you please explain this phenomenon?

...

ANSWER

Answered 2020-Sep-24 at 23:10

While the comments are certainly correct that a model's score on its own training set will be inflated, it is unlikely to get a perfect fit with linear regression, especially with just one feature.

Your problem is that you've reshaped the data incorrectly: reshape(1, -1) makes an array of shape (1, n), so your model thinks it has n features and n outputs with only a single sample, and so is a multiple linear regression with a perfect fit. Try instead with reshape(-1, 1) for x and no reshaping for y.

Source https://stackoverflow.com/questions/64042359

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install ML-From-Scratch

You can install using 'pip install ML-From-Scratch' or download it from GitHub, PyPI.
You can use ML-From-Scratch like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.