Visualizing Cross-validation Code

Visualizing Cross-validation Code  #MachineLearning #dataviz

  • Let us say, you are writing a nice and clean Machine Learning code (e.g. Linear Regression).
  • As the name of the suggests, cross-validation is the next fun thing after learning Linear Regression because it helps to improve your prediction using the K-Fold strategy.
  • But we divide the dataset into equal K parts (K-Folds or cv).
  • Then train the model on the bigger dataset and test on the smaller dataset.
  • This graph represents the k- folds Cross Validation for the Boston dataset with Linear Regression model.


Cross-validation helps to improve your prediction using the K-Fold strategy. What is K-Fold you asked? Check out this post for a visualized explanation.

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Scikit-Learn Cheat Sheet: Python Machine Learning

Scikit-learn cheat sheet: #machinelearning with #Python -

  • Most of you who are learning data science with Python will have definitely heard already about , the open source Python library that implements a wide variety of machine learning, preprocessing, cross-validation and visualization algorithms with the help of a unified interface.
  • If you’re still quite new to the field, you should be aware that machine learning, and thus also this Python library, belong to the must-knows for every aspiring data scientist.
  • This  cheat sheet will introduce you to the basic steps that you need to go through to implement machine learning algorithms successfully: you’ll see how to load in your data, how to preprocess it, how to create your own model to which you can fit your data and predict target labels, how to validate your model and how to tune it further to improve its performance.
  • In short, this cheat sheet will kickstart your data science projects: with the help of code examples, you’ll have created, validated and tuned your machine learning models in no time.
  • In addition, you’ll make use of Python’s data visualization library  to visualize your results.

A handy scikit-learn cheat sheet to machine learning with Python, including code examples.
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Scikit-Learn Cheat Sheet: Python Machine Learning

Cheat sheet: #machinelearning in #Python with scikit-learn -

  • The cheat sheet will kickstart your data science projects: with the help of code examples, you’ll have created, validated and tuned your machine learning models in no time.
  • Begin with our scikit-learn tutorial for beginners , in which you’ll learn in an easy, step-by-step way how to explore handwritten digits data, how to create a model for it, how to fit your data to your model and how to predict target values.
  • If you still have no idea about how scikit-learn works, this machine learning cheat sheet might come in handy to get a quick first idea of the basics that you need to know to get started.
  • The scikit-learn cheat sheet will introduce you to the basic steps that you need to go through to implement machine learning algorithms successfully: you’ll see how to load in your data, how to preprocess it, how to create your own model to which you can fit your data and predict target labels, how to validate your model and how to tune it further to improve its performance.
  • If you’re still quite new to the field, you should be aware that machine learning, and also this Python library, belong to the must-knows for every aspiring data scientist.

A handy scikit-learn cheat sheet to machine learning with Python, including code examples.
Continue reading “Scikit-Learn Cheat Sheet: Python Machine Learning”

Scikit-Learn Tutorial Series – Data Science Central

9-part Scikit-Learn Tutorial Series:  #abdsc #BigData #Python #MachineLearning #DataScience

  • Kaggle released a series with tutorials in their blog.
  • I recommend to anyone who is starting or want to learn more about the tool.
  • by Diego Marinho de Oliveira
  • Data Science Central
  • Scikit-Learn Tutorial Series

Kaggle released a series with tutorials in their blog. I recommend to anyone who is starting or want to learn more about the tool.
Continue reading “Scikit-Learn Tutorial Series – Data Science Central”