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A Beginner’s Guide to Machine Learning: What Aspiring Data Scientists Should Know

A Beginner's Guide to Machine Learning

Before choosing a machine learning algorithm, it's important to know their characteristics to generate desired outputs and build smart systems.

Data science is growing super fast. As the demand for AI-enabled solutions is increasing, delivering smarter systems for industries has become essential. And the correctness and efficiency through machine learning operations must be fulfilled to ensure the developed solutions complete all demands. Hence, applying machine learning algorithms on the given dataset to produce righteous results and train the intelligent system is one of the most essential steps from the entire process.

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A Comprehensive Guide to Random Forest in R

Random Forest in R

Random Forest in R

With the demand for more complex computations, we cannot rely on simplistic algorithms. Instead, we must utilize algorithms with higher computational capabilities and one such algorithm is the random forest. In this blog post on random forest In R, you’ll learn the fundamentals of random forest along with its implementation by using the R Language.

You may also like:  An Introduction to Machine Learning With Decision Trees

What Is Classification?

Classification is the method of predicting the class of a given input data point. Classification problems are common in machine learning and they fall under the Supervised learning method.

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Running Decision Trees in Neo4j

Editor’s Note: This presentation was given by Max De Marzi at GraphConnect 2018 in New York City.

Presentation Summary

In this presentation, Max De Marzi shares how decision trees are used to make near-real-time decisions using a graph database. In this case, he uses the unorthodox example of nightclub entrance criteria.

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Exporting Decision Trees in Textual Format With sklearn

In the past, we have covered Decision Trees showing how interpretable these models can be (see the tutorials here). In the previous tutorials, we exported the rules of the models using the function export_graphviz from sklearn and visualized the output of this function in a graphical way with an external tool which is not easy to install in some cases. Luckily, since version 0.21.2, scikit-learn offers the possibility to export Decision Trees in a textual format (I implemented this feature personally!) and in this post we will see an example how of to use this new feature.

Let's train a tree with two layers on the famous iris dataset using all the data and print the resulting rules using the brand new function export_text: