Here is a comparison for binary classification on imdb sentiment data set. Labels sorted by accuracy score and the heatmap shows the correlation between different approaches. As you can see some methods are good for to ensemble models or perform features selection. ...
Phonetics,Support vector machines,Hidden Markov models,Machine learning,Business,Sentiment analysisOpinion mining is the computational study of people's opinions, emotions and attitudes which is one of the key research field in Natural Language Processing (NLP). To cope with the competitive world, ...
these methods are based on modelling normal or benign behavior of computing devices, networks and systems in the absence of attacks and then using trained models to detect attacks as deviations or anomalous behavior. By design, anomaly
Therefore, we will break down the performance by class (and train separate models to predict each class), and provide F1 and AUC as our major performance metrics, given that accuracy can be misleading in these circumstances. We will not attempt to use sophisticated class balancing approaches in...
FastText is better than other models because of its capability to generalize to unknown words, which had been missing in other methods. FastText provides pre-trained word vectors for different languages, which could be useful in various tasks where we need previous knowledge about words and their ...
Large-language models (LLMs)machinelearning algorithmsASCII-based text vectorizationdimensionality reductionDistinguishing between human and machinegenerated texts has been a task of recent interest in Natural Language Processing (NLP), especially in the face of the malicious use of Large-Language Models ...
The implementation of vectorization methods to compare classification results used the gensim library of Python, which provides ready-made Word2Vec and Doc2Vec models. In order to create a CBOW model, the Word2Vec method is used as follows: model = Word2Vec(documents, vector_size=100, window...
Feature engineering is a critical process in machine learning and data analysis, involving the creation, transformation, and selection of input features to improve the performance and effectiveness of predictive models. It aims to extract relevant information from raw data and represent it in a form ...