It’s widely used in search engines, enterprise search, recommendations and ecommerce. It is also foundational in natural language processing (NLP) to take text data and make it suitable for NLP tasks.Probabilistic ModelsThere is a certain degree of uncertainty in any kind of information ...
Stemming is a technique used for reducing inflected words to their stem or root form. This is applicable for both the suffix as well as prefix. Stemming is a preprocessing step in text mining application and commonly used for Natural Language Processing (NLP). A stemmer can execute operation ...
Data Augmentation is a technique used in neural network design to artificially increase the amount of data to be trained by the model by changing some aspects of the original input data. For example, in the context of image classifiers such as CNN (Convolutional Neural Network), the data augme...
Mean Squared Error (MSE): Commonly used in regression problems. It measures the average squared difference between the predicted and actual values. Cross-Entropy Loss: Used in classification problems, especially when predicting the probability of belonging to a particular class. Hinge Loss: Used for ...
Random forests are made of many decision trees. They are ensembles of decision trees, each decision tree created by using a subset of the attributes used to classify a given population (they are sub-trees, see above). Those decision trees vote on how to classify a given instance of input ...
RQ1: What traditional machine learning algorithms can be used in an exercise model using the evaluated exercises? RQ2: Is it possible to achieve good accuracy using traditional machine learning algorithms in educational exercises? RQ3: What characteristics of the dataset most influence the accuracy ...
metaheuristic algorithms, particularly in nature, emulate nature to clarify optimization problems. Therefore, to tackle realistic optimization issues, performance optimization techniques should be used; however, there is no guarantee that the best solution will be discovered, but they can at their best ...
Prominent examples of modern NLP are language models that use AI and statistics to predict the final form of a sentence on the basis of existing portions. Inlarge language model(LLM), the wordlargerefers to theparameters, or variables and weights, used by the model to influence the prediction...
The authors in [24] used two datasets, one with binary labels and the other with multiclass labels, to explore various natural language processing (NLP) methods to perform sentiment analysis. For the binary classification they applied the bag of words, and skip-gram Word2Vec models followed by...
1. Supervised learning algorithms.Insupervised learning, the algorithm learns from a labeled data set, where the input data is associated with the correct output. This approach is used for tasks such as classification and regression problems such as linear regression, time series regression and logis...