Understand Your Machine Learning Data With Descriptive Statistics in Python

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You must understand your data in order to get the best results.In this post you will discover 7 recipes that you can use in Python to learn more about your machine learning data.Let’s get started.1. Peek at Your DataThere is no substitute for looking at the raw data.Looking at the raw data can reveal insights that you cannot get any other way. It can also plant seeds that may later grow into ideas on how to better preprocess and handle the data for machine learning tasks.You can review the first

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How to Develop a Character-Based Neural Language Model in Keras

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A language model predicts the next word in the sequence based on the specific words that have come before it in the sequence.It is also possible to develop language models at the character level using neural networks. The benefit of character-based language models is their small vocabulary and flexibility in handling any words, punctuation, and other document structure. This comes at the cost of requiring larger models that are slower to train.Nevertheless, in the field of neural language

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3D Plane wire frame Graph

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In this tutorial, we cover how to make a wire frame / plane graph in Matplotlib. For this, we’re just going to use the sample data provided by Matplotlib and leave it there. This type of graph is very specific in its application. If you happen to have your own data, feel free to substitute!The below code covers an example. I have included the commented out function that generates test data for this, in case you are curious:Not going to cover it officially, but check out:Source: Python

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Decision Tree Algorithms Simplified 2

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One of the advantage of using Decision tree is that it efficiently identifies the most significant variable and splits the population on it. In previous article, we developed a high level understanding of Decision trees. In this article, we will focus on the science behind splitting the nodes and choosing the most significant split.Decision trees can use various algorithms to split a node in two or more sub-nodes. The creation of sub-nodes increases the homogeneity of resultant sub-nodes. In

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Decision Tree Simplified!

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What is a Decision Tree? Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It works for both categorical and continuous input and output variables. In this technique, we split the population or sample into two or more homogeneous sets (or sub-populations) based on most significant splitter / differentiator in input variables.Example:-Let’s say we have a sample of 30 students with three variables Gender

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