Scikit-Optimize for Hyperparameter Tuning in Machine Learning

Scikit-Optimize for Hyperparameter Tuning in Machine Learning

Hyperparameter optimization refers to performing a search in order to discover the set of specific model configuration arguments that result in the best performance of the model on a specific dataset. There are many ways to perform hyperparameter optimization, although modern methods, such as Bayesian Optimization, are fast and effective. The Scikit-Optimize library is an […]

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How to Use AutoKeras for Classification and Regression

How to Use AutoKeras for Classification and Regression

AutoML refers to techniques for automatically discovering the best-performing model for a given dataset. When applied to neural networks, this involves both discovering the model architecture and the hyperparameters used to train the model, generally referred to as neural architecture search. AutoKeras is an open-source library for performing AutoML for deep learning models. The search […]

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Multi-Label Classification with Deep Learning

Multi-Label Classification with Deep Learning

Multi-label classification involves predicting zero or more class labels. Unlike normal classification tasks where class labels are mutually exclusive, multi-label classification requires specialized machine learning algorithms that support predicting multiple mutually non-exclusive classes or “labels.” Deep learning neural networks are an example of an algorithm that natively supports multi-label classification problems. Neural network models for […]

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Deep Learning Models for Multi-Output Regression

Deep Learning Models for Multi-Output Regression

Multi-output regression involves predicting two or more numerical variables. Unlike normal regression where a single value is predicted for each sample, multi-output regression requires specialized machine learning algorithms that support outputting multiple variables for each prediction. Deep learning neural networks are an example of an algorithm that natively supports multi-output regression problems. Neural network models […]

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Plot of Actual vs. Predicted Values for Last 12 Months of Car Sales

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 […]

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How to Set NumPy Axis for Rows and Columns in Python

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 […]

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Probability Decision Surface for Logistic Regression on a Binary Classification Task

Plot a Decision Surface for Machine Learning Algorithms in Python

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 […]

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