9/7/2018 komal_digits_recognition_dataset
The digits recognition dataset
In [18]: from sklearn import datasets
import matplotlib.pyplot as plt
import numpy as np
In [4]: # Load the digits dataset: digits
digits = datasets.load_digits()
In [5]: # Print the keys and DESCR of the dataset
print(digits.keys())
dict_keys(['data', 'target', 'target_names', 'images', 'DESCR'])
In [15]: digits['data']
Out[15]: 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.]])
In [16]: digits['target']
Out[16]: array([0, 1, 2, ..., 8, 9, 8])
file:///D:/komal/SIMPLILEARN/MY%20COURSES/IN%20PROGRESS/My%20Codes_ML_DS/codes%20in%20pdf/komal_digits_recognition_dataset.h… 1/4
9/7/2018 komal_digits_recognition_dataset
In [6]: print(digits.DESCR)
Optical Recognition of Handwritten Digits Data Set
===================================================
Notes
-----
Data Set Characteristics:
:Number of Instances: 5620
:Number of Attributes: 64
:Attribute Information: 8x8 image of integer pixels in the range 0..16.
:Missing Attribute Values: None
:Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)
:Date: July; 1998
This is a copy of the test set of the UCI ML hand-written digits datasets
http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Dig
its
The data set contains images of hand-written digits: 10 classes where
each class refers to a digit.
Preprocessing programs made available by NIST were used to extract
normalized bitmaps of handwritten digits from a preprinted form. From a
total of 43 people, 30 contributed to the training set and different 13
to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of
4x4 and the number of on pixels are counted in each block. This generates
an input matrix of 8x8 where each element is an integer in the range
0..16. This reduces dimensionality and gives invariance to small
distortions.
For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.
T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.
L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,
1994.
References
----------
- C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their
Applications to Handwritten Digit Recognition, MSc Thesis, Institute of
Graduate Studies in Science and Engineering, Bogazici University.
- E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.
- Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.
Linear dimensionalityreduction using relevance weighted LDA. School of
Electrical and Electronic Engineering Nanyang Technological University.
2005.
- Claudio Gentile. A New Approximate Maximal Margin Classification
Algorithm. NIPS. 2000.
In [7]: # Print the shape of the images and data keys
print(digits.images.shape)
print(digits.data.shape)
(1797, 8, 8)
(1797, 64)
file:///D:/komal/SIMPLILEARN/MY%20COURSES/IN%20PROGRESS/My%20Codes_ML_DS/codes%20in%20pdf/komal_digits_recognition_dataset.h… 2/4
9/7/2018 komal_digits_recognition_dataset
In [8]: # Display digit 1010
plt.imshow(digits.images[1010], cmap=plt.cm.gray_r, interpolation='nearest')
plt.show()
APPLICATION OF KNN
In [9]: from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
In [10]: X = digits.data
y = digits.target
In [11]: X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, ran
dom_state=42, stratify=y)
In [12]: knn = KNeighborsClassifier(n_neighbors=7)
In [13]: knn.fit(X_train,y_train)
Out[13]: KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=1, n_neighbors=7, p=2,
weights='uniform')
In [14]: print(knn.score(X_test, y_test))
0.9833333333333333
Overfitting and underfitting
file:///D:/komal/SIMPLILEARN/MY%20COURSES/IN%20PROGRESS/My%20Codes_ML_DS/codes%20in%20pdf/komal_digits_recognition_dataset.h… 3/4
9/7/2018 komal_digits_recognition_dataset
In [19]: # Setup arrays to store train and test accuracies
neighbors = np.arange(1, 9)
train_accuracy = np.empty(len(neighbors))
test_accuracy = np.empty(len(neighbors))
# Loop over different values of k
for i, k in enumerate(neighbors):
# Setup a k-NN Classifier with k neighbors: knn
knn = KNeighborsClassifier(n_neighbors=k)
# Fit the classifier to the training data
knn.fit(X_train,y_train)
#Compute accuracy on the training set
train_accuracy[i] = knn.score(X_train,y_train)
#Compute accuracy on the testing set
test_accuracy[i] = knn.score(X_test, y_test)
# Generate plot
plt.title('k-NN: Varying Number of Neighbors')
plt.plot(neighbors, test_accuracy, label = 'Testing Accuracy')
plt.plot(neighbors, train_accuracy, label = 'Training Accuracy')
plt.legend()
plt.xlabel('Number of Neighbors')
plt.ylabel('Accuracy')
plt.show()
OBSERVATIONS MADE: Low value of K --> OVERFITTING High value of K --> UNDERFITTING
file:///D:/komal/SIMPLILEARN/MY%20COURSES/IN%20PROGRESS/My%20Codes_ML_DS/codes%20in%20pdf/komal_digits_recognition_dataset.h… 4/4
