Book Image

Data Science with Python

By : Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen
Book Image

Data Science with Python

By: Rohan Chopra, Aaron England, Mohamed Noordeen Alaudeen

Overview of this book

Data Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression. As you make your way through the book, you will understand the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, discover how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome. By the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.
Table of Contents (10 chapters)

Random Forests

As briefly mentioned earlier, random forests are ensembles of decision trees that can be used to solve classification or regression problems. Random forests use a small portion of the data to fit each tree, so they can handle very large datasets, and they are less prone to the "curse of dimensionality" relative to other algorithms. The curse of dimensionality is a situation in which an abundance of features in the data diminishes the performance of the model. Predictions of the random forest are then determined by combining the predictions of each tree. Like SVM, random forests are a black box with inputs and outputs which cannot be interpreted.

In the upcoming exercises and activities, we will tune and fit a random forest regressor using grid search to predict the temperature in Celsius. Then, we will evaluate the performance of the model.

Exercise 32: Preparing Data for a Random Forest Regressor

First, we will prepare the data for the random forest regressor with...