Keras VS scikit-learn

The report output for scalene does look much nicer, but the slowness for me dropped me from continuing to use it. Maybe there's some bad interaction with tensorflow/pytest. I can try to make an example, but I'd guess if you try running it on tensorflows actual unit tests (something like this) you'd get similar behavior.

One way to improve your projects and coding skills is to try new models and libraries. For example, if you did classification with logistic regression, try also with random forest; if you used Tensorflow, now try Keras; if you scraped a website with BeautifulSoup, now do it with Scrapy. You get the point.

The source for the keras.Model class has grown to be several thousand lines of code. This makes it incredibly challenging to sift through, especially for beginners.

Keras - Keras is an open-source software library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library. Link - https://keras.io/

I think I found my answer here Thank you for your help

Website: https://keras.io/ Github Repository: https://github.com/keras-team/keras Developed By: various Developers, initially by Francois Chollet Primary purpose: Focused on Neural Networks

I also recommend trying to understand the software that's being built by the machine learning class. If you want to build your own machine learning software, check out Keras (http://keras.io/) and the machine learning API's that Keras provides.

I'd say, the first question you'd need to ask yourself is "Why do I want to do statistical tests" and "what kind of statistical tests do I want to do?". Most of them rely on a bunch of assumptions and just winging it will produce a number that will be reported and used but is terribly wrong. Funnily enough, scikit-learn does not directly give you p-values for this very reason and advise you to run the same regression in statsmodels.

Machine learning and statistical analysis? http://scikit-learn.org

Our next step is to create a new machine learning model based on this list. We’ll use Python’s excellent scikit learn framework to build our model. We’ll store our training data into two data frames: one for the set of features to train in and the second with the desired class labels. We’ll then split our dataset into 70% training data and 30% test data.

I can't tell you whether you'd like data science or machine learning, but I can tell you I took a class in it last year. It was an applied ML class targeting power systems engineers. ML is extremely statistics and probability heavy. I personally found the theory to be very dry, but the application to be rather enjoyable. We used sci-kit learn, which is an interesting Python package targeting academic data science and machine learning. https://scikit-learn.org/

scikit-learn for classical machine learning,

Scikit-learn - It is one of the most widely used frameworks for Python based Data science tasks. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Link - https://scikit-learn.org/

Hi all,

Currently some works is being done to improve computational primitives of scikit-learn to enhance its overhaul performances natively.

You can have a look at this exploratory PR: https://github.com/scikit-learn/scikit-learn/pull/20254

This other PR is a clear revamp of this previous one:

Just to clarify, scikit-learn 1.0 has not been released yet. The latest tag in the github repo is 1.0.rc2

https://github.com/scikit-learn/scikit-learn/releases/tag/1....

Website: https://scikit-learn.org/ Github Repository: https://github.com/scikit-learn/scikit-learn Developed By: SkLearn.org Primary Purpose: Predictive Data Analysis and Data Modeling