AI News, Example of logistic regression in Python using scikit-learn
- On Tuesday, March 6, 2018
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Example of logistic regression in Python using scikit-learn
Back in April, I provided a worked example of a real-world linear regression problem using R.
These types of examples can be useful for students getting started in machine learning because they demonstrate both the machine learning workflow and the detailed commands used to execute that workflow.
Here are the steps demonstrated in this example: After viewing the notebook online, you can easily download the notebook and re-run this code on your own computer, especially because the dataset I used is built into statsmodels.
A gallery of interesting Jupyter Notebooks
Important contribution instructions: If you add new content, please ensure that for any notebook you link to, the link is to the rendered version using nbviewer, rather than the raw file.
These are notebooks that use [one of the IPython kernels for other languages](IPython kernels for other languages): The IPython protocols to communicate between kernels and clients are language agnostic, and other programming language communities have started to build support for this protocol in their language.
The interactive plotting library Nyaplot has some case studies using IRuby: This section contains academic papers that have been published in the peer-reviewed literature or pre-print sites such as the ArXiv that include one or more notebooks that enable (even if only partially) readers to reproduce the results of the publication.
Top 5 Python IDEs For Data Science
In addition to that, you can try some alternatives and maybe you’ll even find them better than the ones that are put in the top 5!
For instance, nteract could be a good alternative for those who are looking to focus more on writing a code-driven story.
You already see: instead of working in the browser like with Jupyter, you actually download nteract and execute the application to be able to develop beautiful documents with code, words, and images.
Accessible for beginners is key to the features that you’ll find in nteract: you can execute cells, just like in Jupyter Notebook, but you can also move them around by dragging and dropping them.
Visual Studio contains a feature called IntelliSense, which provides code completions based on variable types, functions and imported modules.
Among its features, Thonny supports code completion and highlight syntax errors, but it also provides a simple debugger, which you can run your program step-by-step.
While editing a function, a new window is opened with local variables and the code being shown separately of your main code.
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You’ll discover how to use these notebooks, how they compare to one another and what other alternatives exist. Contrary to what you might think, Jupyter doesn’t limit you to working solely with Python: the notebook application is language agnostic, which means that you can also work with other languages. There are two general ways to get started on using R with Jupyter: by using a kernel or by setting up an R environment that has all the essential tools to get started on doing data science.
Make sure that you don’t do this in your RStudio console, but in a regular R terminal, otherwise you’ll get an error like this: This command will prompt you to type in a number to select a CRAN mirror to install the necessary packages.
You’ll see R appearing in the list of kernels when you create a new notebook. The second option to quickly work with R is to install the R essentials in your current environment: These “essentials” include the packages dplyr, shiny, ggplot2, tidyr, caret, and nnet.
These commands allow you to switch from Python to command line instructions or to write code in another language such as R, Julia, Scala, … To switch from Python to R, you first need to download the following package: After that, you can get started with R, or you can easily switch from Python to R in your data analysis with the %R magic command.
Let’s demonstrate how the R magic works with a small example: If you want more details about Jupyter, on how to set up a notebook, where to download the application, how you can run the notebook application (via Docker, pip install or with the Anaconda distribution) or other details, check out our Definitive Guide.
Ultimately, you will also realize that this notebook is different from others. In his talk, J.J Allaire, confirms that the efforts in R itself for reproducible research, the efforts of Emacs to combine text code and input, the Pandoc, Markdown and knitr projects, and computational notebooks have been evolving in parallel and influencing each other for a lot of years.
It’s no surprise whatsoever that it is still a core component in the R Markdown Notebook. And there are some things that R Markdown and notebooks share, such as the delivering of a reproducible workflow, the weaving of code, output, and text together in a single document, supporting interactive widgets and outputting to multiple formats.
Note that you can always use the gear icon to adjust the notebook’s working space: you have the option to expand, collapse, and remove the output of your code, to change the preview options and to modify the output options.
You can add code chunks in two ways: through the keyboard shortcut Ctrl + Alt + I or Cmd + Option + I, or with the insert button that you find in the toolbar. What’s great about working with these R Markdown notebooks is the fact that you can follow up on the execution of your code chunks, thanks to the little green bar that appears on the left when you’re executing large code chunks or multiple code chunks at once.
To work with other languages, you need to add separate Bash, Stan, Python, SQL or Rcpp chunks to the notebook. These options might seem quite limited to you, but it’s compensated in the ease with which you can easily add these types of code chunks with the toolbar’s insert button.
By adding some lines to the first section on top of the notebook, you can adjust your output options, like this: To see where you can get those distributions, you can just try to knit, and the console output will give you the sites where you can download the necessary packages. Note that this is just one of the many options that you have to export a notebook: there’s also the possibility to render GitHub documents, word documents, beamer presentation, etc.
You can find more info here. Besides the general coding practices that you should keep in mind, such as documenting your code and applying a consistent naming scheme, code grouping and name length, you can also use the following tips to make a notebook awesome for others to use and read: Besides the differences between the Jupyter and R Markdown notebooks that you have already read above, there are some more things.
This HTML file is an associated file that includes a copy of the R Markdown source code and the generated output. That means that you need no special viewer to see the file, while you might need it to view notebooks that were made with the Jupyter application, which are simple JSON documents, or other computational notebooks that have structured format outputs.
All in all, these code execution options add a considerable amount of flexibility for the users who have been struggling with the code execution options that Jupyter offers, even though if these are not too much different: in the Jupyter application, you have the option to run a single cell, to run cells and to run all cells.
The R Markdown notebooks seem to make this issue a bit easier to handle, as they have associated HTML files that save the output of your code and the fact that the notebook files are essentially plain text files, version control will be much easier.
But this notebook still supports more languages and will be a more suitable companion for you if you’re looking for use Scala, Apache Toree, Julia, or another language. Apart from the notebooks that you can use as interactive data science environments which make it easy for you to share your code with colleagues, peers, and friends, there are also other alternatives to consider.
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