Abatement costs refer to the expenses incurred in reducing or preventing environmental pollution or damage (for example from air emissions or waste disposal). It can be further identified as the total opportunity cost that is incurred by a producer, emitting any emissions, for reducing the emissions from the environment. With growing concerns over climate change […]
Z-test determines to what extent a data point is away from the mean of the data set, in standard deviation.
The Z-Score, also known as standardized score, standard score, or normal score, is the number of units of standard deviation by which the value of a data observation is above or below the mean value of the entire set of values. A z-score equal to 0 means that the data observation is close to the […]
XGBoost is an open source, ensemble machine learning algorithm that utilizes a high-performance implementation of gradient boosted decision trees. An underlying C++ codebase combined with a Python interface sitting on top makes XGBoost a very fast, scalable, and highly usable library.
Web scraping is the process of extracting specific data from websites for further usage. Web scraping can be done automatically by writing a program to capture the necessary information from a website.
The variance is the average squared difference between individual values and the mean of the entire set of values in mathematics and statistics. In other words, variance shows how spread out the values are. In machine learning, the variance is an error caused by the sensitivity of a model to small variations in the training […]
Unsupervised learning is a class of machine learning algorithms that learn the underlying structure of a dataset without being provided a target variable. Unsupervised learning is used to discover common patterns in data, group the values based on their attributes, and then later make predictions on unseen data. The most common unsupervised learning algorithm is […]
Unstructured data is any data that does not fit a predefined data structure such as the typical row-column structure of a database. Examples of such data are images, emails, text documents, videos and audio.
Univariate modeling is the process of modeling the relationship between a single variable, also called predictor, and the target variable. Univariate modeling is usually used with time series.
Underfitting describes a model which does not capture the underlying relationship in the dataset on which it’s trained.
