A tag is a keyword or label that categorizes your question with other, similar questions. Using the right tags makes it easier for others to find and answer your question.

To evaluate is to score or rate the performance of a model, most commonly with a metric like accuracy.
266 questions
Forecasting is the process predicting future values based on historic and current data, typically for time-series datasets.
BERT stands for Bidirectional Encoder Representations from Transformers and is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all l…
Probability is the branch of mathematics concerning numerical descriptions of how likely an event is to occur or how likely it is that a proposition is true.
256 questions
Normalization is a technique often applied as part of data preparation for machine learning. The goal of normalization is to change the values of numeric columns in the dataset to use a common scale, …
Use for questions related to the Transformer (based on encoder-decoder) architecture in machine learning.
A form of signal processing where the input is an image. Usually treating the digital image as a two-dimensional signal (or multidimensional). This processing may include image restoration and enhance…
242 questions
Apache Spark is an open source cluster computing system that aims to make data analytics fast — both fast to run and fast to write, originally developed in the AMPLab at UC Berkeley.
239 questions
Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.
For questions about text classification, the task of assigning predefined categories (or classes) to free-text documents.
Feature scaling is a data pre-processing step where the range of variable values is standardized. Standardization of datasets is a common requirement for many machine learning algorithms. Popular feat…
218 questions
Model selection is the process of comparing several models and their respective results to choose the model is best according to some evaluation metric.
215 questions
Hyperparameter tuning (also called hyperparameter optimization) refers to the process of finding the optimal set of hyperparameters for a given machine learning algorithm.
213 questions
Sentiment analysis refers to categorizing some given data as to what sentiment(s) it expresses. Usually, it refers to extracting sentiment from a text, e.g. tweets or blog posts.
Use for questions about Orange, the free, open-source, component-based, data mining and machine learning software suite.
204 questions
GAN refers to Generative Adversarial Networks. Such networks is made of two networks that compete against each other. The first one generates new samples and the second one discriminates between ge…
203 questions
For the nodes and links sense of graph; use the visualization tag for the charting sense.
201 questions
For questions regarding outliers or unusual points in the data.
A metric is a way to evaluate the performance of a machine learning model. Depending on the task, different metrics may be used.
191 questions
Matplotlib is a plotting library for Python which may be used interactively or embedded in stand-alone GUIs. Its compact "pyplot" interface is similar to the plotting functions of MATLAB®.
185 questions
Naive Bayes classifiers makes the naive assumption that the features are independent. They make use of Bayes theorem.
185 questions
Transfer learning is the process of learning a set of characteristics from one data and applying this "knowledge" to another similar dataset (i.e. using the same model across datasets).
178 questions
Mathematics in a data science or machine learning context refers to the mathematical underpinnings for algorithms, optimization, statistics, and linear algebra etc.
178 questions
K-Nearest Neighbor (K-NN) is a classification algorithm that determines the label of some data point based on the most common label of the closest k other points.
Missing data is a problem that arises in data science when some data contained in rows or columns may be missing or unavailable for some samples in a dataset.
166 questions
In machine learning, ensemble methods combine multiple algorithms to make a prediction. Bagging, boosting, and stacking, are some examples.
163 questions
For Question about Performance of a data science, statistical or machine learning model. Performace is a direct way to measure the efficiency of model. The Performance measure deals with time, accurac…
163 questions
MATLAB is a high-level language and interactive programming environment for numerical computation and visualization developed by MathWorks. Don’t use both the [matlab] and [octave] tags, unless the qu…
157 questions
A confusion matrix is a special contingency table used to evaluate the predictive accuracy of a classifier. Predicted classes are listed in rows and actual classes in columns, with counts of respectiv…
152 questions
Encoding in machine learning and data science refers to the process by which non-numeric data is transformed into a numeric representation that can be fed into machine learning algorithms.
152 questions
Inclusion of additional constraints (typically a penalty for complexity) in the model fitting process. Used to prevent overfitting / enhance predictive accuracy.
152 questions
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