How to Use XGBoost for Time Series Forecasting

Last Updated on August 27, 2020 XGBoost is an efficient implementation of gradient boosting for classification and regression problems. It is both fast and efficient, performing well, if not the best, on a wide range of predictive modeling tasks and is a favorite among data science competition winners, such as those on Kaggle. XGBoost can also be used for time series forecasting, although it requires that the time series dataset be transformed into a supervised learning problem first. It also […]

September 29, 2020 Machine Learning

Multi-Class Imbalanced Classification

Last Updated on August 21, 2020 Imbalanced classification are those prediction tasks where the distribution of examples across class labels is not equal. Most imbalanced classification examples focus on binary classification tasks, yet many of the tools and techniques for imbalanced classification also directly support multi-class classification problems. In this tutorial, you will discover how to use the tools of imbalanced classification with a multi-class dataset. After completing this tutorial, you will know: About the glass identification standard imbalanced multi-class […]

September 29, 2020 Machine Learning

How to use Seaborn Data Visualization for Machine Learning

Last Updated on August 19, 2020 Data visualization provides insight into the distribution and relationships between variables in a dataset. This insight can be helpful in selecting data preparation techniques to apply prior to modeling and the types of algorithms that may be most suited to the data. Seaborn is a data visualization library for Python that runs on top of the popular Matplotlib data visualization library, although it provides a simple interface and aesthetically better-looking plots. In this tutorial, […]

September 29, 2020 Machine Learning

A Gentle Introduction to Computational Learning Theory

Last Updated on September 7, 2020 Computational learning theory, or statistical learning theory, refers to mathematical frameworks for quantifying learning tasks and algorithms. These are sub-fields of machine learning that a machine learning practitioner does not need to know in great depth in order to achieve good results on a wide range of problems. Nevertheless, it is a sub-field where having a high-level understanding of some of the more prominent methods may provide insight into the broader task of learning […]

September 29, 2020 Machine Learning

Plot a Decision Surface for Machine Learning Algorithms in Python

Last Updated on August 26, 2020 Classification algorithms learn how to assign class labels to examples, although their decisions can appear opaque. A popular diagnostic for understanding the decisions made by a classification algorithm is the decision surface. This is a plot that shows how a fit machine learning algorithm predicts a coarse grid across the input feature space. A decision surface plot is a powerful tool for understanding how a given model “sees” the prediction task and how it […]

September 29, 2020 Machine Learning

Why Do I Get Different Results Each Time in Machine Learning?

Last Updated on August 27, 2020 Are you getting different results for your machine learning algorithm? Perhaps your results differ from a tutorial and you want to understand why. Perhaps your model is making different predictions each time it is trained, even when it is trained on the same data set each time. This is to be expected and might even be a feature of the algorithm, not a bug. In this tutorial, you will discover why you can expect […]

September 29, 2020 Machine Learning

How to Calculate the Bias-Variance Trade-off with Python

Last Updated on August 26, 2020 The performance of a machine learning model can be characterized in terms of the bias and the variance of the model. A model with high bias makes strong assumptions about the form of the unknown underlying function that maps inputs to outputs in the dataset, such as linear regression. A model with high variance is highly dependent upon the specifics of the training dataset, such as unpruned decision trees. We desire models with low […]

September 29, 2020 Machine Learning

Hypothesis Test for Comparing Machine Learning Algorithms

Last Updated on September 1, 2020 Machine learning models are chosen based on their mean performance, often calculated using k-fold cross-validation. The algorithm with the best mean performance is expected to be better than those algorithms with worse mean performance. But what if the difference in the mean performance is caused by a statistical fluke? The solution is to use a statistical hypothesis test to evaluate whether the difference in the mean performance between any two algorithms is real or […]

September 29, 2020 Machine Learning

How to Set Axis for Rows and Columns in NumPy

NumPy arrays provide a fast and efficient way to store and manipulate data in Python. They are particularly useful for representing data as vectors and matrices in machine learning. Data in NumPy arrays can be accessed directly via column and row indexes, and this is reasonably straightforward. Nevertheless, sometimes we must perform operations on arrays of data such as sum or mean of values by row or column and this requires the axis of the operation to be specified. Unfortunately, […]

September 29, 2020 Machine Learning

Time Series Forecasting With Prophet in Python

Time series forecasting can be challenging as there are many different methods you could use and many different hyperparameters for each method. The Prophet library is an open-source library designed for making forecasts for univariate time series datasets. It is easy to use and designed to automatically find a good set of hyperparameters for the model in an effort to make skillful forecasts for data with trends and seasonal structure by default. In this tutorial, you will discover how to […]

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