The pixel values in images must be scaled prior to providing the images as input to a deep learning neural network model during the training or evaluation of the model. Traditionally, the images would have to be scaled prior to the development of the model and stored in memory or on disk in the scaled […]

# Search results for "normalize"

## How to Normalize and Standardize Time Series Data in Python

Some machine learning algorithms will achieve better performance if your time series data has a consistent scale or distribution. Two techniques that you can use to consistently rescale your time series data are normalization and standardization. In this tutorial, you will discover how you can apply normalization and standardization rescaling to your time series data […]

## How to Normalize and Standardize Your Machine Learning Data in Weka

Machine learning algorithms make assumptions about the dataset you are modeling. Often, raw data is comprised of attributes with varying scales. For example, one attribute may be in kilograms and another may be a count. Although not required, you can often get a boost in performance by carefully choosing methods to rescale your data. In […]

## Softmax Activation Function with Python

Softmax is a mathematical function that converts a vector of numbers into a vector of probabilities, where the probabilities of each value are proportional to the relative scale of each value in the vector. The most common use of the softmax function in applied machine learning is in its use as an activation function in […]

## Radius Neighbors Classifier Algorithm With Python

Radius Neighbors Classifier is a classification machine learning algorithm. It is an extension to the k-nearest neighbors algorithm that makes predictions using all examples in the radius of a new example rather than the k-closest neighbors. As such, the radius-based approach to selecting neighbors is more appropriate for sparse data, preventing examples that are far […]

## Linear Discriminant Analysis With Python

Linear Discriminant Analysis is a linear classification machine learning algorithm. The algorithm involves developing a probabilistic model per class based on the specific distribution of observations for each input variable. A new example is then classified by calculating the conditional probability of it belonging to each class and selecting the class with the highest probability. […]

## How to Hill Climb the Test Set for Machine Learning

Hill climbing the test set is an approach to achieving good or perfect predictions on a machine learning competition without touching the training set or even developing a predictive model. As an approach to machine learning competitions, it is rightfully frowned upon, and most competition platforms impose limitations to prevent it, which is important. Nevertheless, […]

## Hyperparameter Optimization With Random Search and Grid Search

Machine learning models have hyperparameters that you must set in order to customize the model to your dataset. Often the general effects of hyperparameters on a model are known, but how to best set a hyperparameter and combinations of interacting hyperparameters for a given dataset is challenging. There are often general heuristics or rules of […]

## How to Selectively Scale Numerical Input Variables for Machine Learning

Many machine learning models perform better when input variables are carefully transformed or scaled prior to modeling. It is convenient, and therefore common, to apply the same data transforms, such as standardization and normalization, equally to all input variables. This can achieve good results on many problems. Nevertheless, better results may be achieved by carefully […]

## How to Grid Search Data Preparation Techniques

Machine learning predictive modeling performance is only as good as your data, and your data is only as good as the way you prepare it for modeling. The most common approach to data preparation is to study a dataset and review the expectations of a machine learning algorithms, then carefully choose the most appropriate data […]