| --- |
| layout: toc |
| --- |
| |
| // Licensed to the Apache Software Foundation (ASF) under one or more |
| // contributor license agreements. See the NOTICE file distributed with |
| // this work for additional information regarding copyright ownership. |
| // The ASF licenses this file to You under the Apache License, Version 2.0 |
| // (the "License"); you may not use this file except in compliance with |
| // the License. You may obtain a copy of the License at |
| // |
| // http://www.apache.org/licenses/LICENSE-2.0 |
| // |
| // Unless required by applicable law or agreed to in writing, software |
| // distributed under the License is distributed on an "AS IS" BASIS, |
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| // See the License for the specific language governing permissions and |
| // limitations under the License. |
| |
| = Introduction |
| |
| In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. |
| |
| All existing training algorithms presented in this section are designed to solve binary classification tasks: |
| |
| |
| * Linear SVM (Support Vector Machines) |
| * Decision Trees |
| * Multilayer perceptron |
| * Logistic Regression |
| * k-NN Classification |
| * ANN (Approximate Nearest Neighbor) |
| * Naive Bayes |
| |
| |
| Binary or binomial classification is the task of classifying the elements of a given set into two groups (predicting which group each one belongs to) on the basis of a classification rule. |
