CIABURRO MATLAB PDF

Extract patterns and knowledge from your data in easy way using MATLAB About This Book Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn Learn the introductory concepts of machine learning. Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures.

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We use synthetic data to create a clear example of how the decision boundary of logistic regression looks in comparison to the training samples. Since there are two features, we can say that the data for this problem are two-dimensional.

This makes it easy to visualize. Perform the following steps: Generate the features using the following code: np. Notice, however, that we are also going to assign the true class at the same time. The result of this is that we have 20 samples each in the positive and negative classes, for a total of 40 samples, and that we have two features for each sample. We show the first three values of each feature for both positive and negative classes.

The output should be the following: Generating synthetic data for a binary classification problem Plot these data, coloring the positive samples in red and the negative samples in blue. The plotting code is as follows: plt. There will be 40 rows because there are 40 total samples, and 2 columns because there are 2 features. We will arrange things so that the features for the positive samples come in the first 20 rows, and those for the negative samples after that.

Create a vertical stack vstack of 20 1s and then 20 0s to match our arrangement of the features and reshape to the way that scikit-learn expects. We will use all of the data as training data and examine how well a linear model is able to fit the data. First, import the model class using the following code: from sklearn. Use this code to get predictions and separate them into indices of positive and negative class predictions.

To know, how to install the required packages to set up a data science coding environment, read the book Data Science Projects with Python on Packt Publishing.

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