Algorithm

Autoregressive integrated moving average (ARIMA) Autoregressive integrated moving average (ARIMA) is a statistical model that is used to forecast future values of a time series. It is a generalization of the autoregressive moving average (ARMA) model, which only considers the autocorrelations between the current value and the past values of the time series. ARIMA also considers the moving average of the residuals of the ARMA model, which helps to improve the accuracy of the forecasts....

Softmax Activation Function The softmax activation function is a non-linear function that is commonly used in the output layer of neural networks for multi-class classification problems. It takes a vector of real numbers as input and outputs a vector of probabilities, where the probabilities sum to 1. This means that the softmax function can be used to represent a probability distribution over the possible output classes. The softmax function is defined as follows:...

Analytics Random Cut Forest (RCF) Analytics Random Cut Forest (RCF) is an unsupervised machine learning algorithm that is used for anomaly detection. In RCF, each tree is constructed by randomly selecting a subset of features and then randomly selecting a subset of data points from the training dataset. This process helps to ensure that the trees are more diverse and less correlated, which makes them more effective at detecting anomalies....

Understanding Different Classification Loss Function Types in Machine Learning In machine learning, classification problems are quite common and central to many applications. The purpose of a classification problem is to predict discrete class labels, such as detecting if an email is spam or not, identifying the species of a flower based on measurements, or recognizing handwritten digits. While designing a classification model, a key part to consider is the choice of the loss function, which measures how well the model’s predictions align with the true values....