title: How can do I print accuracy and validation accuracy for my SVM model?
body: <p>I'm currently trying to see the difference between the accuracy and validation accuracy for my SVM model, so that I can see if over fitting or under fitting has occurred. However, the output of my model shows like this:</p>
  <pre><code>Evaluating classifier on test data ...
                precision    recall  f1-score   support
  
             0       1.00      0.20      0.33         5
             1       0.79      1.00      0.88        15
  
      accuracy                           0.80        20
     macro avg       0.89      0.60      0.61        20
  weighted avg       0.84      0.80      0.75        20
  
  Validation Accuracy: 0.8
  </code></pre>
  <p>How can I see the difference between my model's accuracy and validation accuracy? Do I need to have another line of code to print only its accuracy?</p>
  <p>(edited)Below the code of my model:</p>
  <pre><code>from skimage.feature import hog, local_binary_pattern
  from skimage.transform import pyramid_gaussian
  from skimage.io import imread
  import joblib
  from sklearn.preprocessing import LabelEncoder
  from sklearn import svm
  from sklearn.metrics import classification_report
  from sklearn.model_selection import train_test_split
  from skimage import color
  from imutils.object_detection import non_max_suppression
  import imutils
  import numpy as np
  import argparse
  import cv2
  import os
  import glob
  from sklearn import metrics
  from PIL import Image # This will be used to read/modify images (can be done via OpenCV too)
  from numpy import *
  
  # define parameters of HOG feature extraction
  orientations = 9
  pixels_per_cell = (8, 8)
  cells_per_block = (2, 2)
  threshold = .3
  
  
  # define path to images:
  
  dataset_path = r&quot;dataset&quot; # This is the path of our dataset
  
  # read the image files:
  category_im_listing = os.listdir(dataset_path) # it will read all the files in the path
  num_category_im = size(category_im_listing) # simply states the total no. of category
  print(&quot;There are &quot; + str(num_category_im) + &quot; categories&quot;) # prints the number value of the no.of categories dataset
  data= []
  labels = []
  count = 0
  
  # compute HOG features and label them:
  
  for category in category_im_listing: #this loop enables reading the files in the pos_im_listing variable one by one
      im_listing = os.listdir(dataset_path + &quot;/&quot; + category)
      num_im = size(im_listing)
      print(&quot;There are &quot; + str(num_im) + &quot; images in category &quot; + str(count + 1))
      for file in im_listing:
          img = Image.open(dataset_path + &quot;/&quot; + category + &quot;/&quot; + file) # open the file
          img = img.resize((150,150))
          gray = img.convert('L') # convert the image into single channel i.e. RGB to grayscale
          # calculate HOG for positive features
          fd = hog(gray, orientations, pixels_per_cell, cells_per_block, block_norm='L2', feature_vector=True)# fd= feature descriptor
          data.append(fd)
          labels.append(count)
      count = count + 1
  
  # encode the labels, converting them from strings to integers
  le = LabelEncoder()
  labels = le.fit_transform(labels)
  #%%
  # Partitioning the data into training and testing splits, using 80%
  # of the data for training and the remaining 20% for testing
  print(&quot; Constructing training/testing split...&quot;)
  (trainData, testData, trainLabels, testLabels) = train_test_split(
      np.array(data), labels, test_size=0.20, random_state=42)
  #%% Train the linear SVM
  print(&quot; Training Linear SVM classifier with HOG...&quot;)
  model = svm.LinearSVC(multi_class='ovr')
  model.fit(trainData, trainLabels)
  #%% Evaluate the classifier
  print(&quot; Evaluating classifier on test data ...&quot;)
  predictions = model.predict(testData)
  print(classification_report(testLabels, predictions))
  print(&quot;Validation Accuracy:&quot;,metrics.accuracy_score(testLabels, predictions))
  </code></pre>