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Text Classification in NLP using Cross Validation and BERT

Mlearning.ai

Prediction of Solar Irradiation Using Quantum Support Vector Machine Learning Algorithm. Submission Suggestions Text Classification in NLP using Cross Validation and BERT was originally published in MLearning.ai Smart Grid and Renewable Energy , 07 (12), 293–301. link] Ganaie, M. Tanveer, M., & Suganthan, P.

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An Essential Introduction to SVM Algorithm in Machine Learning

Pickl AI

Summary: Support Vector Machine (SVM) is a supervised Machine Learning algorithm used for classification and regression tasks. Among the many algorithms, the SVM algorithm in Machine Learning stands out for its accuracy and effectiveness in classification tasks. What is the SVM Algorithm in Machine Learning?

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Top 10 Data Science Interviews Questions and Expert Answers

Pickl AI

Machine Learning Algorithms Candidates should demonstrate proficiency in a variety of Machine Learning algorithms, including linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks. What is cross-validation, and why is it used in Machine Learning?

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How To Improve Machine Learning Model Accuracy

DagsHub

This can be done by training machine learning algorithms such as logistic regression, decision trees, random forests, and support vector machines on a dataset containing categorical outputs. For example, you might want to build a ML model that determines if an email is spam or not.

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Gender detection from sound, How machine learning works?

Mlearning.ai

Data Preprocessing: The extracted features may undergo preprocessing steps such as normalization, scaling, or dimensionality reduction to ensure compatibility and optimal performance for the machine learning model. Training a Machine Learning Model : The preprocessed features are used to train a machine learning model.

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Bias and Variance in Machine Learning

Pickl AI

Unstable Support Vector Machines (SVM) Support Vector Machines can be prone to high variance if the kernel used is too complex or if the cost parameter is not properly tuned. Regular cross-validation and model evaluation are essential to maintain this equilibrium.

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Predicting Heart Failure Survival with Machine Learning Models — Part II

Towards AI

(Check out the previous post to get a primer on the terms used) Outline Dealing with Class Imbalance Choosing a Machine Learning model Measures of Performance Data Preparation Stratified k-fold Cross-Validation Model Building Consolidating Results 1. among supervised models and k-nearest neighbors, DBSCAN, etc.,