Pagerank Algorithm Eigenvalue
Square Matrices
Geometric Interpretation
Eigenvalue:
https://www.khanacademy.org/math/linear-algebra/alternate-bases/eigen-everything/v/linear-algebra-introduction-to-eigenvalues-and-eigenvectors
Pagerank Algorithm Eigenvalue
Square Matrices
Geometric Interpretation
Eigenvalue:
https://www.khanacademy.org/math/linear-algebra/alternate-bases/eigen-everything/v/linear-algebra-introduction-to-eigenvalues-and-eigenvectors
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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
Posted in Machine Learning
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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,'') |
https://www.youtube.com/watch?v=rifK8BtHaYI
Must See:
Posted in Machine Learning, Research, Statistics
Tagged Machine Learning, Statistics
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:
Deep Learning, Deep Neural Network:
Posted in Data Science, Machine Learning
@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
Different terms for: