Evaluating the performance of classification models is crucial in machine learning, as it helps us understand how well our models are making predictions. One of the most effective ways to do this is by using a confusion matrix, a simple yet powerful tool that provides insights into the types of errors a model makes. In […]
Confusion Matrix for Machine Learning in Python Read More »
The median absolute deviation (MAD), is a robust statistic of variability that measures the spread of a dataset. In this tutorial, you’ll learn how to use Python to calculate the median absolute deviation. By the end of this tutorial, you’ll have learned: What the Median Absolute Deviation is and how to interpret it How to
How to Calculate the Median Absolute Deviation in Python Read More »
In this tutorial, you’ll learn everything you need to know about the important and powerful PyTorch DataLoader class. PyTorch provides an intuitive and incredibly versatile tool, the DataLoader class, to load data in meaningful ways. Because data preparation is a critical step to any type of data work, being able to work with, and understand,
PyTorch DataLoader: A Complete Guide Read More »
In this tutorial, you’ll learn how to implement the sigmoid activation function in Python. Because the sigmoid function is an activation function in neural networks, it’s important to understand how to implement it in Python. You’ll also learn some of the key attributes of the sigmoid function and why it’s such a useful function in
Implementing the Sigmoid Function in Python Read More »
In this tutorial, you’ll learn about Support Vector Machines (or SVM) and how they are implemented in Python using Sklearn. The support vector machine algorithm is a supervised machine learning algorithm that is often used for classification problems, though it can also be applied to regression problems. This tutorial assumes no prior knowledge of the
Support Vector Machines (SVM) in Python with Sklearn Read More »
In this tutorial, you’ll learn how to use the OneHotEncoder class in Scikit-Learn to one hot encode your categorical data in sklearn. One-hot encoding is a process by which categorical data (such as nominal data) are converted into numerical features of a dataset. This is often a required preprocessing step since machine learning models require
One-Hot Encoding in Scikit-Learn with OneHotEncoder Read More »
In this tutorial, you’ll learn how to calculate the mean absolute error, or MAE, in Python. The mean absolute error can help measure the accuracy of a given machine learning model. The MAE can be a good complement or alternative to the mean squared error (MSE). By the end of this tutorial, you’ll have learned: What
How to Calculate Mean Absolute Error (MAE) in Python Read More »
In this tutorial, you’ll learn how all you need to know about the K-Nearest Neighbor algorithm and how it works using Scikit-Learn in Python. The K-Nearest Neighbor algorithm in this tutorial will focus on classification problems, though many of the principles will work for regression as well. The tutorial assumes no prior knowledge of the
K-Nearest Neighbor (KNN) Algorithm in Python Read More »
In this tutorial, you’ll learn how to use Python to calculate the MAPE, or the mean absolute percentage error. This error is often used to measure the accuracy of machine learning models. By the end of this tutorial, you’ll have learned: What the Mean Absolute Percentage Error is What a good value for the MAPE
How to Calculate MAPE in Python Read More »
In this tutorial, you’ll learn how to use GridSearchCV for hyper-parameter tuning in machine learning. In machine learning, you train models on a dataset and select the best performing model. One of the tools available to you in your search for the best model is Scikit-Learn’s GridSearchCV class. By the end of this tutorial, you’ll
Hyper-parameter Tuning with GridSearchCV in Sklearn Read More »
In this tutorial, you’ll learn how to use Python to calculate the Manhattan distance. The Manhattan distance is often referred to as the city block distance or the taxi cab distance. The Manhattan distance can be a helpful measure when working with high dimensional datasets. By the end of this tutorial, you’ll have learned: What
Calculate Manhattan Distance in Python (City Block Distance) Read More »
In this tutorial, you’ll learn how to calculate the hamming distance in Python, using step-by-step examples. In machine learning, the Hamming distance represents the sum of corresponding elements that differ between vectors. By the end of this tutorial, you’ll have learned: Common applications of the Hamming Distance in machine learning, How to calculate the Hamming
Calculate Hamming Distance in Python (with Examples) Read More »
