摘要： Latent Profile Analysis (LPA) tries to identify clusters of individuals (i.e., latent profiles) based on responses to a series of continuous variables (i.e., indicators). LPA assumes that there are unobserved latent profiles that generate patterns of responses on indicator items.
摘要： In this article we’re going to take a look at the 3 most common loss functions for Machine Learning Regression. I’ll explain how they work, their pros and cons, and how they can be most effectively applied when training regression models.
摘要： DataStax Apache Cassandra as a Service, a Database as a Service offering, and DataStax Insights, a performance management service, to be first of platform’s offerings built to simplify application development
Summary： Outlining some of the common pitfalls of machine learning for time series forecasting, with a look at time delayed predictions, autocorrelations, stationarity, accuracy metrics, and more.
Summary： Many think performance problems don’t exist in the cloud, but they do. Deal with them the same way you did before cloud. 許多人以為雲端不存在性能問題，但這些問題確實存在。
摘要： Here a list of resources, mostly in the form of tutorials, covering most important topics in data science: This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, dataviz, AI and many more.
