Assignment 5: Multi-Classification
Due date: Mar 13th, 2020 (Friday)
Total Points: 100
Please put your name, student ID, date and time here
Name:
Student ID:
Date:
Time:
In this assignment, you will investigate the handwritten digits dataset.
Sample images:
Please apply the folowing eight methods to classify the handwritten digits dataset.
Split the dataset into training sets and test sets
Fit the training data sets to the following eight algorithms
Print the classification report on the test data sets
Method 1: KNN
Method 2: Linear SVM
Method 3: Gaussian Kernel SVM
Method 4: Naive Bayes
Method 5: Decision Tree
Method 6: Random Forest
Method 7: Voting Classifier
Method 8: Bagging
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In [4]: # Importing the dataset
from sklearn.datasets import load_digits
digits = load_digits()
print(digits)
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{‘data’: array([[ 0., 0., 5., …, 0., 0., 0.],
[ 0., 0., 0., …, 10., 0., 0.],
[ 0., 0., 0., …, 16., 9., 0.],
…,
[ 0., 0., 1., …, 6., 0., 0.],
[ 0., 0., 2., …, 12., 0., 0.],
[ 0., 0., 10., …, 12., 1., 0.]]), ‘target’: array([0, 1, 2, …, 8,
9, 8]), ‘target_names’: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), ‘images’: array
([[[ 0., 0., 5., …, 1., 0., 0.],
[ 0., 0., 13., …, 15., 5., 0.],
[ 0., 3., 15., …, 11., 8., 0.],
…,
[ 0., 4., 11., …, 12., 7., 0.],
[ 0., 2., 14., …, 12., 0., 0.],
[ 0., 0., 6., …, 0., 0., 0.]],
[[ 0., 0., 0., …, 5., 0., 0.],
[ 0., 0., 0., …, 9., 0., 0.],
[ 0., 0., 3., …, 6., 0., 0.],
…,
[ 0., 0., 1., …, 6., 0., 0.],
[ 0., 0., 1., …, 6., 0., 0.],
[ 0., 0., 0., …, 10., 0., 0.]],
[[ 0., 0., 0., …, 12., 0., 0.],
[ 0., 0., 3., …, 14., 0., 0.],
[ 0., 0., 8., …, 16., 0., 0.],
…,
[ 0., 9., 16., …, 0., 0., 0.],
[ 0., 3., 13., …, 11., 5., 0.],
[ 0., 0., 0., …, 16., 9., 0.]],
…,
[[ 0., 0., 1., …, 1., 0., 0.],
[ 0., 0., 13., …, 2., 1., 0.],
[ 0., 0., 16., …, 16., 5., 0.],
…,
[ 0., 0., 16., …, 15., 0., 0.],
[ 0., 0., 15., …, 16., 0., 0.],
[ 0., 0., 2., …, 6., 0., 0.]],
[[ 0., 0., 2., …, 0., 0., 0.],
[ 0., 0., 14., …, 15., 1., 0.],
[ 0., 4., 16., …, 16., 7., 0.],
…,
[ 0., 0., 0., …, 16., 2., 0.],
[ 0., 0., 4., …, 16., 2., 0.],
[ 0., 0., 5., …, 12., 0., 0.]],
[[ 0., 0., 10., …, 1., 0., 0.],
[ 0., 2., 16., …, 1., 0., 0.],
[ 0., 0., 15., …, 15., 0., 0.],
…,
[ 0., 4., 16., …, 16., 6., 0.],
[ 0., 8., 16., …, 16., 8., 0.],
[ 0., 1., 8., …, 12., 1., 0.]]]), ‘DESCR’: “.. _digits_dataset:\n\
nOptical recognition of handwritten digits dataset\n
————————————————–\n\n**Data Set Characteristic
s:**\n\n :Number of Instances: 5620\n :Number of Attributes: 64\n :Attr
ibute Information: 8×8 image of integer pixels in the range 0..16.\n :Missing
Attribute Values: None\n :Creator: E. Alpaydin (alpaydin ‘@’ boun.edu.tr)\n
:Date: July; 1998\n\nThis is a copy of the test set of the UCI ML hand-written d
igits datasets\nhttp://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Ha
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In [27]: import matplotlib.pyplot as plt
digits.images[0].shape
list = [10,100,100,45]
fig = plt.figure()
for i,j in enumerate(list):
plt.subplot(2,2,i+1)
plt.imshow(digits.images[j],cmap=’gray’)
In [2]: X = digits.data
y = digits.target
Step 1. Split the dataset into training data and testing data ( 10
points )
In [ ]:
Step 2. Algorithm Analysis ( 80 points )
Method 1. KNN
In [ ]:
Method 2. Linear SVM
In [ ]:
Method 3. Gaussian Kernal SVM
In [ ]:
Method 4. Naive Bayes
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In [ ]:
Method 5. Decision Tree
In [ ]:
Method 6. Random Forest
In [ ]:
Method 7. Voting Classifier
In [ ]:
Method 8. Bagging
In [ ]:
Step 3: Accuracy Results Table ( 8 points )
KNN L_SVM RBF_SVM NB DT RF Voting Bagging
Accuracy
Weighted Precision
Weighted Recall
Step 4: Conclusion ( 2 Points )
In [ ]:
In [ ]:
In [ ]:
In [