Tutorial by Examples: classification

Imagine that a system want to detect apples and oranges in a basket of fruits. System can pick a fruit, extract some property of it (e.g weight of that fruit). Suppose System has a Teacher! that teaches the system which objects are apples and which are oranges. This is an example of a supervised cl...

Build a text report showing the main classification metrics, including the precision and recall, f1-score (the harmonic mean of precision and recall) and support (the number of observations of that class in the training set). Example from sklearn docs: from sklearn.metrics import classification_re...

In this example, a model will learn to classify fruits given certain features, using the Labels for training. WeightColorLabel0.5greenapple0.6purpleplum3greenwatermelon0.1redcherry0.5redapple Here the a model will take Weight and Color as features to predict the Label. For instance [0.15, 'red'] s...

In SQL Server, there are two categories of triggers: DDL Triggers and DML Triggers. DDL Triggers are fired in response to Data Definition Language (DDL) events. These events primarily correspond to Transact-SQL statements that start with the keywords CREATE, ALTER and DROP. DML Triggers are fired ...

Caffe has a build-in input layer tailored for image classification tasks (i.e., single integer label per input image). This input "Data" layer is built upon an lmdb or leveldb data structure. In order to use "Data" layer one has to construct the data structure with all training d...

Classification in Machine Learning is the problem that identifies to which set of categories does a new observation belong. Classification falls under the category of Supervised Machine Learning. Any algorithm that implements classification is known as classifier The classifiers supported in P...

Consider you want to predict the correct answer for XOR popular problem. You Knew what is XOR(e.g [x0 x1] => y). for example [0 0] => 0, [0 1] => [1] and... #Load Sickit learn data from sklearn.neighbors import KNeighborsClassifier #X is feature vectors, and y is correct label(To train...

1. Bagged Decision Trees Bagging performs best with algorithms that have high variance. A popular example are decision trees, often constructed without pruning. In the example below see an example of using the BaggingClassifier with the Classification and Regression Trees algorithm (DecisionTreeCl...

Create training instances from .arff file private static Instances getDataFromFile(String path) throws Exception{ DataSource source = new DataSource(path); Instances data = source.getDataSet(); if (data.classIndex() == -1){ data.setClassIndex(data.numAttributes()...

###### Used for both Classification and Regression examples library(randomForest) library(car) ## For the Soils data data(Soils) ###################################################### ## RF Classification Example set.seed(656) ## for ...

In LR Classifier, he probabilities describing the possible outcomes of a single trial are modeled using a logistic function. It is implemented in the linear_model library from sklearn.linear_model import LogisticRegression The sklearn LR implementation can fit binary, One-vs- Rest, or multinomia...

For this example, let's assume that the inputs have a dimensionality of (frames, channels, rows, columns), and the outputs have a dimensionality of (classes). from keras.applications.vgg16 import VGG16 from keras.models import Model from keras.layers import Dense, Input from keras.layers.pooling...

I am using eclipse here, and you need to add below given dependency to your pom.xml 1.) POM.XML <project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://m...