In this post we will be using Python to import files. Importing a text file is rather easy into Python. We will look at several different examples and file types in this post. Importing a Text File Importing a text file is often done in Python. To do this see ...

RANSAC regression is a unique style of regression. This algorithm identifies outliers and inliers using the unique tools of this approach. The video below provides an overview of how it can be used in Python

In this video, we will look at gradient boosting classification with python. Gradient boosting is similar to Adaboost in that it is an ensemble technique and is often associated with decision trees. The main difference is the focus on the gradient or slope in the calculations.

AdaBoost regression uses ensemble learning to improve the performance of numeric prediction models. The video below explains how to use adaBoost with Python.

AdaBoost classification is a type of ensemble learning. What this means is that the algorithm makes multiple models that work together to make predictions. Such techniques are powerful in improving the strength of models. The video below explains how to use this algorithm within Python.

Elastic net regression has all the strengths of both ridge and lasso regression without the apparent weaknesses. As such this is a great algorithm for regularized regression. The video below explains how to use this algorithm with Python

Lasso regression is another algorithm that uses regularization to handle variables. Essentially, this algorithm will reduce coefficients to zero based on whether they contribute meaningfully to the results. The video below will explain how to use Lasso regression in Python.

Ridge regression belongs to a family of regression called regularization regression. This family of regression uses various mathematical techniques to reduce or remove coefficients from a regression model. In the case of ridge, this algorithm will reduce coefficients close to zero but never actually remove variables from a model. In ...

Hyper-parameter tuning is one way of taking your model development to the next level. This tool provides several ways to make small adjustments that can reap huge benefits. In the video below, we will look at tuning the hyper-parameters of a KNN model. Naturally, this tuning process can be used ...

Cross-validation is a valuable tool for assessing a model’s ability to generalize. In the video below, we will look at how to use cross-validation with Python.