Articles About Machine Learning

Tune Hyperparameters for Classification Machine Learning Algorithms

Last Updated on August 28, 2020 Machine learning algorithms have hyperparameters that allow you to tailor the behavior of the algorithm to your specific dataset. Hyperparameters are different from parameters, which are the internal coefficients or weights for a model found by the learning algorithm. Unlike parameters, hyperparameters are specified by the practitioner when configuring the model. Typically, it is challenging to know what values to use for the hyperparameters of a given algorithm on a given dataset, therefore it […]

September 29, 2020 Machine Learning

How to Transform Target Variables for Regression in Python

Last Updated on August 18, 2020 Data preparation is a big part of applied machine learning. Correctly preparing your training data can mean the difference between mediocre and extraordinary results, even with very simple linear algorithms. Performing data preparation operations, such as scaling, is relatively straightforward for input variables and has been made routine in Python via the Pipeline scikit-learn class. On regression predictive modeling problems where a numerical value must be predicted, it can also be critical to scale […]

September 29, 2020 Machine Learning

Arithmetic, Geometric, and Harmonic Means for Machine Learning

Last Updated on August 19, 2020 Calculating the average of a variable or a list of numbers is a common operation in machine learning. It is an operation you may use every day either directly, such as when summarizing data, or indirectly, such as a smaller step in a larger procedure when fitting a model. The average is a synonym for the mean, a number that represents the most likely value from a probability distribution. As such, there are multiple […]

September 29, 2020 Machine Learning

Best Results for Standard Machine Learning Datasets

Last Updated on August 28, 2020 It is important that beginner machine learning practitioners practice on small real-world datasets. So-called standard machine learning datasets contain actual observations, fit into memory, and are well studied and well understood. As such, they can be used by beginner practitioners to quickly test, explore, and practice data preparation and modeling techniques. A practitioner can confirm whether they have the data skills required to achieve a good result on a standard machine learning dataset. A […]

September 29, 2020 Machine Learning

TensorFlow 2 Tutorial: Get Started in Deep Learning With tf.keras

Last Updated on August 27, 2020 Predictive modeling with deep learning is a skill that modern developers need to know. TensorFlow is the premier open-source deep learning framework developed and maintained by Google. Although using TensorFlow directly can be challenging, the modern tf.keras API beings the simplicity and ease of use of Keras to the TensorFlow project. Using tf.keras allows you to design, fit, evaluate, and use deep learning models to make predictions in just a few lines of code. […]

September 29, 2020 Machine Learning

How to Use the ColumnTransformer for Data Preparation

Last Updated on August 18, 2020 You must prepare your raw data using data transforms prior to fitting a machine learning model. This is required to ensure that you best expose the structure of your predictive modeling problem to the learning algorithms. Applying data transforms like scaling or encoding categorical variables is straightforward when all input variables are the same type. It can be challenging when you have a dataset with mixed types and you want to selectively apply data […]

September 29, 2020 Machine Learning

A Gentle Introduction to Imbalanced Classification

Last Updated on January 14, 2020 Classification predictive modeling involves predicting a class label for a given observation. An imbalanced classification problem is an example of a classification problem where the distribution of examples across the known classes is biased or skewed. The distribution can vary from a slight bias to a severe imbalance where there is one example in the minority class for hundreds, thousands, or millions of examples in the majority class or classes. Imbalanced classifications pose a […]

September 29, 2020 Machine Learning

Best Resources for Imbalanced Classification

Last Updated on January 14, 2020 Classification is a predictive modeling problem that involves predicting a class label for a given example. It is generally assumed that the distribution of examples in the training dataset is even across all of the classes. In practice, this is rarely the case. Those classification predictive models where the distribution of examples across class labels is not equal (e.g. are skewed) are called “imbalanced classification.” Typically, a slight imbalance is not a problem and […]

September 29, 2020 Machine Learning

Develop an Intuition for Severely Skewed Class Distributions

Last Updated on January 14, 2020 An imbalanced classification problem is a problem that involves predicting a class label where the distribution of class labels in the training dataset is not equal. A challenge for beginners working with imbalanced classification problems is what a specific skewed class distribution means. For example, what is the difference and implication for a 1:10 vs. a 1:100 class ratio? Differences in the class distribution for an imbalanced classification problem will influence the choice of […]

September 29, 2020 Machine Learning

Standard Machine Learning Datasets for Imbalanced Classification

Last Updated on January 14, 2020 An imbalanced classification problem is a problem that involves predicting a class label where the distribution of class labels in the training dataset is skewed. Many real-world classification problems have an imbalanced class distribution, therefore it is important for machine learning practitioners to get familiar with working with these types of problems. In this tutorial, you will discover a suite of standard machine learning datasets for imbalanced classification. After completing this tutorial, you will […]

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