Topic: polynomial-kernel Goto Github
Some thing interesting about polynomial-kernel
Some thing interesting about polynomial-kernel
polynomial-kernel,Machine Learning Code Implementations in Python
User: amishasomaiya
polynomial-kernel,We will apply soft-margin SVM to handwritten digits from the processed US Postal Service Zip Code data set.
User: ayanpahari
polynomial-kernel,Linear Regression with L2 Regularization, Online, Average, and Polynomial Kernel Perceptron for Optical Character Recognition, Decision Tree Ensemble, Random Forest, AdaBoost
User: being-aerys
polynomial-kernel,kernalized t-Distributed Stochastic Neighbor Embedding (t-SNE)
User: danshai
polynomial-kernel,Created a model from scratch (without using any libraries) to predict whether a person have a heart diseases using support vector machine. and then compare the model's accuracy with model created using Sklearn library.
User: gajju4
polynomial-kernel,Building a smodel using SVC
User: gulshank0719
polynomial-kernel,Support Vector Machines (SVMs in short) are supervised machine learning algorithms that are used for classification and regression purposes. In this kernel, I have build a Support Vector Machines classifier to classify a Pulsar star. I have used the Predicting a Pulsar Star dataset for this project.
User: gulshank0719
polynomial-kernel,Second assignment of Artificial Intelligence course held by Professor Andrea Torsello of Ca' Foscari University of Venice, spam detectors with SVM classification using linear, polynomial of degree 2, RBF kernels and Naive Bayes and k-NN
User: lurenss
polynomial-kernel,Classification of tweets as positive or negative sentiments using different SVM kernels
User: opeajibuwa
polynomial-kernel,SVMs are used for Classification as well as Regression problems. However, it is primarily used for Classification problems. #%% md # The Technique (Support Vector Machine) Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, it is used for Classification problems in Machine Learning. The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane.
User: parashar7
polynomial-kernel,This code reads a dataset i.e, "Heart.csv". Preprocessing of dataset is done and we divide the dataset into training and testing datasets. Linear, rbf and Polynomial kernel SVC are applied and accuracy scores are calculated on the test data. Also, a graph is plotted to show change of accuracy with change in "C" value.
User: saipavani68
polynomial-kernel,Project for Machine Learning Data Mining course
User: saralittlesquirrel
polynomial-kernel,A SVM classifier created to classify data on the IRIS dataset. A linear SVM as well as a Radial Basis SVM was created and also a polynomial kernel was created which classifies the data correctly to their original class.
User: saumyabhandari
polynomial-kernel,Hyperparameter tuning using Support Vector Machine kernels
User: sushama-rangarajan
polynomial-kernel,This project focuses on classifying pulsar stars using the Support Vector Machine (SVM) algorithm, a powerful method in the realm of supervised learning. The goal is to automate the identification process of pulsar stars from candidates collected during surveys, based on predictive modeling.
User: zahirahmadchaudhry
polynomial-kernel,This repository contains codes for running naive bayes and k-NN classification algorithms on large dataset in python
User: znreza
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