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Category Archives: Machine Learning
Machine Learning with Python
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# Naive Bayes # Importing the libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd #importing the dataset dataset=pd.read_csv('clusterincluster.csv'); columns=dataset.iloc[:, [0,1]].values; # here first colon means selecting all rows then after comma means selecting feature/column so manually we can enter 0 and 1 and we can also put -1 to select all rows except the last one as we are making the last one as class #independent variables label=dataset.iloc[:, [2]].values; # dependent variables index in python starts from 0 #splitting the dataset into the traiing set and test set from sklearn.cross_validation import train_test_split columns_train, columns_test, label_train, label_test=train_test_split(columns,label, test_size=0.2, random_state=0) #if 10 observations/sample then 0.2 means two observations in a test set and 8 observations in test set |
Posted in Machine Learning
Machine Learning Mastery
The Naive Bayes algorithm is simple and effective and should be one of the first methods you try on a classification problem. In this tutorial you are going to learn about the Naive Bayes algorithm including how it works and how to implement it from scratch in Python. Update: Check out the follow-up on tips for …
Posted in Machine Learning
Machine Learning in MATLAB: KNN
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dataset=csvread('F:\Machine Learning Training\OneDrive-2017-10-28\Datasets\clusterincluster.csv',1,0); label = dataset(:,3); dataset = dataset(:, 1:2); rng(2); Mdl =fitcnb(dataset, label, 'CrossVal', 'on','kfold',10); Mdl =fitcdiscr(dataset, label, 'CrossVal', 'on','kfold',10); yfit=kfoldPredict(Mdl); conf=confusionmat(label,yfit); gscatter(meas(:1),meas(:,2),species,'') |
Statistics: For Machine Learning: Random Variables : Discrete and Continuous
https://www.youtube.com/watch?v=rifK8BtHaYI
Must See:
Probability is the measure of how likely an event is to occur out of the number of possible outcomes. This wikiHow will show you how to calculate different types of probabilities. Define your events and outcomes. Probability is the…
Posted in Machine Learning, Research, Statistics
Tagged Machine Learning, Statistics
Training Set, Validation Set, Test Set
Training Set is a subset of the dataset used to build predictive models.
Validation Set is a subset of the dataset used to assess the performance of model built in the training phase
– It provides a test platform for fine-tuning model’s parameters and selecting the best performing model
– Not all modeling algorithms need a validation set
Test set or unseen examples is a subset of the dataset to assess the likely future performance of a model.
– If a model fits the training set much better than it fits the test set. Overfitting is probably the cause
Binary Classification(two class classification)
true|false, 1|0, -1|+1, male|female
Multi-class classification problems can be seen as binary classification problems.
Model Evaluation:
Machine Learning in Easy Way
Deep Learning, Deep Neural Network:
Posted in Data Science, Machine Learning
WEKA
@relation weather
@attribute outlook {sunny, overcast, rainy}
@attribute temperature numeirc
@attribute humidity numeric
@attribute windy {TRUE,FALSE}
@attribute play {yes,no}
@data
sunny, 90, 77, TRUE, no
overcast, 88, 90, FALSE, no
Difference between AI, Machine Learning, NLP and Deep Learning
Different terms for:
Difference between AI, Machine Learning, NLP and Deep Learning
Different terms for: