While chi-square tests are very powerful, they are often misused. This hypothesis test is commonly used to test three different things. Chi-Square Goodness of Fit Test Used to test if a categorical variable is from a distribution Chi-Square Test of Independence Used to test if …
Evaluating a clustering algorithm is much different than evaluating a classification or regression machine learning algorithm. In a classification problem, labels will be given for the data points, giving you a reference point to create your accuracy measure. What happens in unsupervised clustering algorithms when …
In data science, when trying to discover the trends and patterns inside of data, you may run into many different scenarios. For example, you could run into a situation where the data is not linear, you have more than one variable (multivariate), and you seem …
While K means clustering is one of the most famous clustering algorithms, what happens when you are clustering categorical variables or dealing with binary data? K means clustering depends on the data points being continuous, where an average (mean) is easy to compute, and the …
Topic modeling is a dense but gratifying subject. Knowing how to put your text into specific topics is crucial to understanding the different genres within your text. Utilizing topic modeling also allows you to quickly ingest ideas from your text no matter the size of …
Seeing what features are most important in your models is key to optimizing and increasing model accuracy. Feature importance is one of the most crucial aspects of machine learning, and sometimes how you got to an answer is more important than the output. Below we’ll …
Creating different machine learning models in Keras becomes super easy once we understand the fundamentals. Getting the correct output shape starts with correctly defining the right input shape for your deep learning models. If you mess this up, you’ll spend a ton of time googling …
Keras, while powerful, does have many different hyperparameters to choose from. Messing up steps_per_epoch while modeling with the .fit method in Keras can create a ton of problems. This guide will show you what steps_per_epoch does, how to figure out the correct number of steps, …
The Dense layer is a critical component in Machine Learning. While the most straightforward layer, the dense layer is still vital in any neural network design and is one of the most commonly used layers. Below we will be breaking down the output generated from …
Deep learning can be tricky, but we have some APIs that help us create wonderful models that can quickly converge to a great solution. The Keras API used for neural networks has risen in popularity for modeling with TensorFlow. Keras Shuffle is easy to mess …
