So what happens if your ML model is obviously flawed? I.E. it predicts the flip of a coin to always be heads, etc. One potential problem is that the model parameters need to be tweaked from its defaults. A way to fix this problem is to utilize a grid search, in which we test multiple combinations of parameters to see which produce the most accurate results. In this example we’re utilizing a breast cancer dataset already present in the scikit learn library. We’re going to predict if values fall in the ‘target’ field or not (present as a binary 0 or 1).

Grid Searching in Python - How-to

I.E. the default parameters for a model aren't working so well...

In this example we're utilizing a breast cancer dataset already present in the scikit learn library. We're going to predict if values fall in the 'target' field or not (present as a binary 0 or 1).

In [16]:
#Imports
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
In [17]:
#Import data
from sklearn.datasets import load_breast_cancer

cancer = load_breast_cancer()
In [18]:
#Some info on this dataset
print(cancer['DESCR'])
.. _breast_cancer_dataset:

Breast cancer wisconsin (diagnostic) dataset
--------------------------------------------

**Data Set Characteristics:**

    :Number of Instances: 569

    :Number of Attributes: 30 numeric, predictive attributes and the class

    :Attribute Information:
        - radius (mean of distances from center to points on the perimeter)
        - texture (standard deviation of gray-scale values)
        - perimeter
        - area
        - smoothness (local variation in radius lengths)
        - compactness (perimeter^2 / area - 1.0)
        - concavity (severity of concave portions of the contour)
        - concave points (number of concave portions of the contour)
        - symmetry
        - fractal dimension ("coastline approximation" - 1)

        The mean, standard error, and "worst" or largest (mean of the three
        worst/largest values) of these features were computed for each image,
        resulting in 30 features.  For instance, field 0 is Mean Radius, field
        10 is Radius SE, field 20 is Worst Radius.

        - class:
                - WDBC-Malignant
                - WDBC-Benign

    :Summary Statistics:

    ===================================== ====== ======
                                           Min    Max
    ===================================== ====== ======
    radius (mean):                        6.981  28.11
    texture (mean):                       9.71   39.28
    perimeter (mean):                     43.79  188.5
    area (mean):                          143.5  2501.0
    smoothness (mean):                    0.053  0.163
    compactness (mean):                   0.019  0.345
    concavity (mean):                     0.0    0.427
    concave points (mean):                0.0    0.201
    symmetry (mean):                      0.106  0.304
    fractal dimension (mean):             0.05   0.097
    radius (standard error):              0.112  2.873
    texture (standard error):             0.36   4.885
    perimeter (standard error):           0.757  21.98
    area (standard error):                6.802  542.2
    smoothness (standard error):          0.002  0.031
    compactness (standard error):         0.002  0.135
    concavity (standard error):           0.0    0.396
    concave points (standard error):      0.0    0.053
    symmetry (standard error):            0.008  0.079
    fractal dimension (standard error):   0.001  0.03
    radius (worst):                       7.93   36.04
    texture (worst):                      12.02  49.54
    perimeter (worst):                    50.41  251.2
    area (worst):                         185.2  4254.0
    smoothness (worst):                   0.071  0.223
    compactness (worst):                  0.027  1.058
    concavity (worst):                    0.0    1.252
    concave points (worst):               0.0    0.291
    symmetry (worst):                     0.156  0.664
    fractal dimension (worst):            0.055  0.208
    ===================================== ====== ======

    :Missing Attribute Values: None

    :Class Distribution: 212 - Malignant, 357 - Benign

    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian

    :Donor: Nick Street

    :Date: November, 1995

This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.
https://goo.gl/U2Uwz2

Features are computed from a digitized image of a fine needle
aspirate (FNA) of a breast mass.  They describe
characteristics of the cell nuclei present in the image.

Separating plane described above was obtained using
Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree
Construction Via Linear Programming." Proceedings of the 4th
Midwest Artificial Intelligence and Cognitive Science Society,
pp. 97-101, 1992], a classification method which uses linear
programming to construct a decision tree.  Relevant features
were selected using an exhaustive search in the space of 1-4
features and 1-3 separating planes.

The actual linear program used to obtain the separating plane
in the 3-dimensional space is that described in:
[K. P. Bennett and O. L. Mangasarian: "Robust Linear
Programming Discrimination of Two Linearly Inseparable Sets",
Optimization Methods and Software 1, 1992, 23-34].

This database is also available through the UW CS ftp server:

ftp ftp.cs.wisc.edu
cd math-prog/cpo-dataset/machine-learn/WDBC/

.. topic:: References

   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction 
     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on 
     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,
     San Jose, CA, 1993.
   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and 
     prognosis via linear programming. Operations Research, 43(4), pages 570-577, 
     July-August 1995.
   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques
     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) 
     163-171.
In [19]:
#Convert to dataframe
df_feat = pd.DataFrame(cancer['data'],columns=cancer['feature_names'])
In [20]:
df_feat.head()
Out[20]:
mean radius mean texture mean perimeter mean area mean smoothness mean compactness mean concavity mean concave points mean symmetry mean fractal dimension ... worst radius worst texture worst perimeter worst area worst smoothness worst compactness worst concavity worst concave points worst symmetry worst fractal dimension
0 17.99 10.38 122.80 1001.0 0.11840 0.27760 0.3001 0.14710 0.2419 0.07871 ... 25.38 17.33 184.60 2019.0 0.1622 0.6656 0.7119 0.2654 0.4601 0.11890
1 20.57 17.77 132.90 1326.0 0.08474 0.07864 0.0869 0.07017 0.1812 0.05667 ... 24.99 23.41 158.80 1956.0 0.1238 0.1866 0.2416 0.1860 0.2750 0.08902
2 19.69 21.25 130.00 1203.0 0.10960 0.15990 0.1974 0.12790 0.2069 0.05999 ... 23.57 25.53 152.50 1709.0 0.1444 0.4245 0.4504 0.2430 0.3613 0.08758
3 11.42 20.38 77.58 386.1 0.14250 0.28390 0.2414 0.10520 0.2597 0.09744 ... 14.91 26.50 98.87 567.7 0.2098 0.8663 0.6869 0.2575 0.6638 0.17300
4 20.29 14.34 135.10 1297.0 0.10030 0.13280 0.1980 0.10430 0.1809 0.05883 ... 22.54 16.67 152.20 1575.0 0.1374 0.2050 0.4000 0.1625 0.2364 0.07678

5 rows × 30 columns

Split data into test/train, and predict utilizing Support Vector Machines (SVN) modeling

In [21]:
#Import splitting library
from sklearn.model_selection import train_test_split
In [22]:
#Set X,Y
X = df_feat
y = cancer['target']
In [23]:
#Choose the test size
#Test size = % of dataset allocated for testing (.3 = 30%)
#Random state = # of random splits
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)
In [24]:
#Import library
from sklearn.svm import SVC

At this point I'm going to instantiate an SVC() object, and at the point of writing this tutorial the default 'C' value is '1.0' and the default 'gamma' value is 'scale'. In this case, those values actually produce a very good model with precision of ~0.93. So, I'm going to force the gamma value to 'auto' instead - which will produce an undesired result

In [25]:
#Create object, force gamma to auto so we can see a bad prediction
model = SVC(gamma='auto')
In [26]:
#Fit
model.fit(X_train,y_train)
Out[26]:
SVC(gamma='auto')
In [27]:
#Predict
predictions = model.predict(X_test)
In [28]:
#See if the model worked, print reports (worked very well)
from sklearn.metrics import classification_report, confusion_matrix
In [29]:
print(confusion_matrix(y_test,predictions))
[[  0  66]
 [  0 105]]
In [30]:
print(classification_report(y_test,predictions))
              precision    recall  f1-score   support

           0       0.00      0.00      0.00        66
           1       0.61      1.00      0.76       105

    accuracy                           0.61       171
   macro avg       0.31      0.50      0.38       171
weighted avg       0.38      0.61      0.47       171

C:\Users\kaled\Anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))

Notice that it predicted all of my values to fall into '1' which in this case isn't possible.

In [31]:
#Show bad predictions
predictions
Out[31]:
array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])

Fix model by determining best parameters with Grid Searching

In [33]:
#Imports
from sklearn.model_selection import GridSearchCV

We're going to focus on testing out what combination of 'C' and 'gamma' values produce the best result

C = controls cost of misclassification on training data. Large C value gives low bias and high variance
gamma = a large gamma gives you high bias and low variance

In [34]:
#Provide a dictionary of these values to test
param_grid = {'C':[0.1,1,10,100,1000],'gamma':[1,0.1,0.01,0.001,0.0001]}
In [36]:
#Instantiate object
grid = GridSearchCV(SVC(),param_grid,verbose=3)
In [37]:
#Fit to find the best combo of params
grid.fit(X_train,y_train)
Fitting 5 folds for each of 25 candidates, totalling 125 fits
[CV] C=0.1, gamma=1 ..................................................
[CV] ...................... C=0.1, gamma=1, score=0.637, total=   0.0s
[CV] C=0.1, gamma=1 ..................................................
[CV] ...................... C=0.1, gamma=1, score=0.637, total=   0.0s
[CV] C=0.1, gamma=1 ..................................................
[CV] ...................... C=0.1, gamma=1, score=0.625, total=   0.0s
[CV] C=0.1, gamma=1 ..................................................
[CV] ...................... C=0.1, gamma=1, score=0.633, total=   0.0s
[CV] C=0.1, gamma=1 ..................................................
[CV] ...................... C=0.1, gamma=1, score=0.633, total=   0.0s
[CV] C=0.1, gamma=0.1 ................................................
[CV] .................... C=0.1, gamma=0.1, score=0.637, total=   0.0s
[CV] C=0.1, gamma=0.1 ................................................
[CV] .................... C=0.1, gamma=0.1, score=0.637, total=   0.0s
[CV] C=0.1, gamma=0.1 ................................................
[CV] .................... C=0.1, gamma=0.1, score=0.625, total=   0.0s
[CV] C=0.1, gamma=0.1 ................................................
[CV] .................... C=0.1, gamma=0.1, score=0.633, total=   0.0s
[CV] C=0.1, gamma=0.1 ................................................
[CV] .................... C=0.1, gamma=0.1, score=0.633, total=   0.0s
[CV] C=0.1, gamma=0.01 ...............................................
[CV] ................... C=0.1, gamma=0.01, score=0.637, total=   0.0s
[CV] C=0.1, gamma=0.01 ...............................................
[CV] ................... C=0.1, gamma=0.01, score=0.637, total=   0.0s
[CV] C=0.1, gamma=0.01 ...............................................
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s
[CV] ................... C=0.1, gamma=0.01, score=0.625, total=   0.0s
[CV] C=0.1, gamma=0.01 ...............................................
[CV] ................... C=0.1, gamma=0.01, score=0.633, total=   0.0s
[CV] C=0.1, gamma=0.01 ...............................................
[CV] ................... C=0.1, gamma=0.01, score=0.633, total=   0.0s
[CV] C=0.1, gamma=0.001 ..............................................
[CV] .................. C=0.1, gamma=0.001, score=0.637, total=   0.0s
[CV] C=0.1, gamma=0.001 ..............................................
[CV] .................. C=0.1, gamma=0.001, score=0.637, total=   0.0s
[CV] C=0.1, gamma=0.001 ..............................................
[CV] .................. C=0.1, gamma=0.001, score=0.625, total=   0.0s
[CV] C=0.1, gamma=0.001 ..............................................
[CV] .................. C=0.1, gamma=0.001, score=0.633, total=   0.0s
[CV] C=0.1, gamma=0.001 ..............................................
[CV] .................. C=0.1, gamma=0.001, score=0.633, total=   0.0s
[CV] C=0.1, gamma=0.0001 .............................................
[CV] ................. C=0.1, gamma=0.0001, score=0.887, total=   0.0s
[CV] C=0.1, gamma=0.0001 .............................................
[CV] ................. C=0.1, gamma=0.0001, score=0.938, total=   0.0s
[CV] C=0.1, gamma=0.0001 .............................................
[CV] ................. C=0.1, gamma=0.0001, score=0.963, total=   0.0s
[CV] C=0.1, gamma=0.0001 .............................................
[CV] ................. C=0.1, gamma=0.0001, score=0.962, total=   0.0s
[CV] C=0.1, gamma=0.0001 .............................................
[CV] ................. C=0.1, gamma=0.0001, score=0.886, total=   0.0s
[CV] C=1, gamma=1 ....................................................
[CV] ........................ C=1, gamma=1, score=0.637, total=   0.0s
[CV] C=1, gamma=1 ....................................................
[CV] ........................ C=1, gamma=1, score=0.637, total=   0.0s
[CV] C=1, gamma=1 ....................................................
[CV] ........................ C=1, gamma=1, score=0.625, total=   0.0s
[CV] C=1, gamma=1 ....................................................
[CV] ........................ C=1, gamma=1, score=0.633, total=   0.0s
[CV] C=1, gamma=1 ....................................................
[CV] ........................ C=1, gamma=1, score=0.633, total=   0.0s
[CV] C=1, gamma=0.1 ..................................................
[CV] ...................... C=1, gamma=0.1, score=0.637, total=   0.0s
[CV] C=1, gamma=0.1 ..................................................
[CV] ...................... C=1, gamma=0.1, score=0.637, total=   0.0s
[CV] C=1, gamma=0.1 ..................................................
[CV] ...................... C=1, gamma=0.1, score=0.625, total=   0.0s
[CV] C=1, gamma=0.1 ..................................................
[CV] ...................... C=1, gamma=0.1, score=0.633, total=   0.0s
[CV] C=1, gamma=0.1 ..................................................
[CV] ...................... C=1, gamma=0.1, score=0.633, total=   0.0s
[CV] C=1, gamma=0.01 .................................................
[CV] ..................... C=1, gamma=0.01, score=0.637, total=   0.0s
[CV] C=1, gamma=0.01 .................................................
[CV] ..................... C=1, gamma=0.01, score=0.637, total=   0.0s
[CV] C=1, gamma=0.01 .................................................
[CV] ..................... C=1, gamma=0.01, score=0.625, total=   0.0s
[CV] C=1, gamma=0.01 .................................................
[CV] ..................... C=1, gamma=0.01, score=0.633, total=   0.0s
[CV] C=1, gamma=0.01 .................................................
[CV] ..................... C=1, gamma=0.01, score=0.633, total=   0.0s
[CV] C=1, gamma=0.001 ................................................
[CV] .................... C=1, gamma=0.001, score=0.900, total=   0.0s
[CV] C=1, gamma=0.001 ................................................
[CV] .................... C=1, gamma=0.001, score=0.912, total=   0.0s
[CV] C=1, gamma=0.001 ................................................
[CV] .................... C=1, gamma=0.001, score=0.925, total=   0.0s
[CV] C=1, gamma=0.001 ................................................
[CV] .................... C=1, gamma=0.001, score=0.962, total=   0.0s
[CV] C=1, gamma=0.001 ................................................
[CV] .................... C=1, gamma=0.001, score=0.937, total=   0.0s
[CV] C=1, gamma=0.0001 ...............................................
[CV] ................... C=1, gamma=0.0001, score=0.912, total=   0.0s
[CV] C=1, gamma=0.0001 ...............................................
[CV] ................... C=1, gamma=0.0001, score=0.950, total=   0.0s
[CV] C=1, gamma=0.0001 ...............................................
[CV] ................... C=1, gamma=0.0001, score=0.975, total=   0.0s
[CV] C=1, gamma=0.0001 ...............................................
[CV] ................... C=1, gamma=0.0001, score=0.962, total=   0.0s
[CV] C=1, gamma=0.0001 ...............................................
[CV] ................... C=1, gamma=0.0001, score=0.937, total=   0.0s
[CV] C=10, gamma=1 ...................................................
[CV] ....................... C=10, gamma=1, score=0.637, total=   0.0s
[CV] C=10, gamma=1 ...................................................
[CV] ....................... C=10, gamma=1, score=0.637, total=   0.0s
[CV] C=10, gamma=1 ...................................................
[CV] ....................... C=10, gamma=1, score=0.625, total=   0.0s
[CV] C=10, gamma=1 ...................................................
[CV] ....................... C=10, gamma=1, score=0.633, total=   0.0s
[CV] C=10, gamma=1 ...................................................
[CV] ....................... C=10, gamma=1, score=0.633, total=   0.0s
[CV] C=10, gamma=0.1 .................................................
[CV] ..................... C=10, gamma=0.1, score=0.637, total=   0.0s
[CV] C=10, gamma=0.1 .................................................
[CV] ..................... C=10, gamma=0.1, score=0.637, total=   0.0s
[CV] C=10, gamma=0.1 .................................................
[CV] ..................... C=10, gamma=0.1, score=0.625, total=   0.0s
[CV] C=10, gamma=0.1 .................................................
[CV] ..................... C=10, gamma=0.1, score=0.633, total=   0.0s
[CV] C=10, gamma=0.1 .................................................
[CV] ..................... C=10, gamma=0.1, score=0.633, total=   0.0s
[CV] C=10, gamma=0.01 ................................................
[CV] .................... C=10, gamma=0.01, score=0.637, total=   0.0s
[CV] C=10, gamma=0.01 ................................................
[CV] .................... C=10, gamma=0.01, score=0.637, total=   0.0s
[CV] C=10, gamma=0.01 ................................................
[CV] .................... C=10, gamma=0.01, score=0.613, total=   0.0s
[CV] C=10, gamma=0.01 ................................................
[CV] .................... C=10, gamma=0.01, score=0.633, total=   0.0s
[CV] C=10, gamma=0.01 ................................................
[CV] .................... C=10, gamma=0.01, score=0.633, total=   0.0s
[CV] C=10, gamma=0.001 ...............................................
[CV] ................... C=10, gamma=0.001, score=0.887, total=   0.0s
[CV] C=10, gamma=0.001 ...............................................
[CV] ................... C=10, gamma=0.001, score=0.912, total=   0.0s
[CV] C=10, gamma=0.001 ...............................................
[CV] ................... C=10, gamma=0.001, score=0.900, total=   0.0s
[CV] C=10, gamma=0.001 ...............................................
[CV] ................... C=10, gamma=0.001, score=0.937, total=   0.0s
[CV] C=10, gamma=0.001 ...............................................
[CV] ................... C=10, gamma=0.001, score=0.924, total=   0.0s
[CV] C=10, gamma=0.0001 ..............................................
[CV] .................. C=10, gamma=0.0001, score=0.950, total=   0.0s
[CV] C=10, gamma=0.0001 ..............................................
[CV] .................. C=10, gamma=0.0001, score=0.912, total=   0.0s
[CV] C=10, gamma=0.0001 ..............................................
[CV] .................. C=10, gamma=0.0001, score=0.975, total=   0.0s
[CV] C=10, gamma=0.0001 ..............................................
[CV] .................. C=10, gamma=0.0001, score=0.949, total=   0.0s
[CV] C=10, gamma=0.0001 ..............................................
[CV] .................. C=10, gamma=0.0001, score=0.949, total=   0.0s
[CV] C=100, gamma=1 ..................................................
[CV] ...................... C=100, gamma=1, score=0.637, total=   0.0s
[CV] C=100, gamma=1 ..................................................
[CV] ...................... C=100, gamma=1, score=0.637, total=   0.0s
[CV] C=100, gamma=1 ..................................................
[CV] ...................... C=100, gamma=1, score=0.625, total=   0.0s
[CV] C=100, gamma=1 ..................................................
[CV] ...................... C=100, gamma=1, score=0.633, total=   0.0s
[CV] C=100, gamma=1 ..................................................
[CV] ...................... C=100, gamma=1, score=0.633, total=   0.0s
[CV] C=100, gamma=0.1 ................................................
[CV] .................... C=100, gamma=0.1, score=0.637, total=   0.0s
[CV] C=100, gamma=0.1 ................................................
[CV] .................... C=100, gamma=0.1, score=0.637, total=   0.0s
[CV] C=100, gamma=0.1 ................................................
[CV] .................... C=100, gamma=0.1, score=0.625, total=   0.0s
[CV] C=100, gamma=0.1 ................................................
[CV] .................... C=100, gamma=0.1, score=0.633, total=   0.0s
[CV] C=100, gamma=0.1 ................................................
[CV] .................... C=100, gamma=0.1, score=0.633, total=   0.0s
[CV] C=100, gamma=0.01 ...............................................
[CV] ................... C=100, gamma=0.01, score=0.637, total=   0.0s
[CV] C=100, gamma=0.01 ...............................................
[CV] ................... C=100, gamma=0.01, score=0.637, total=   0.0s
[CV] C=100, gamma=0.01 ...............................................
[CV] ................... C=100, gamma=0.01, score=0.613, total=   0.0s
[CV] C=100, gamma=0.01 ...............................................
[CV] ................... C=100, gamma=0.01, score=0.633, total=   0.0s
[CV] C=100, gamma=0.01 ...............................................
[CV] ................... C=100, gamma=0.01, score=0.633, total=   0.0s
[CV] C=100, gamma=0.001 ..............................................
[CV] .................. C=100, gamma=0.001, score=0.887, total=   0.0s
[CV] C=100, gamma=0.001 ..............................................
[CV] .................. C=100, gamma=0.001, score=0.912, total=   0.0s
[CV] C=100, gamma=0.001 ..............................................
[CV] .................. C=100, gamma=0.001, score=0.900, total=   0.0s
[CV] C=100, gamma=0.001 ..............................................
[CV] .................. C=100, gamma=0.001, score=0.937, total=   0.0s
[CV] C=100, gamma=0.001 ..............................................
[CV] .................. C=100, gamma=0.001, score=0.924, total=   0.0s
[CV] C=100, gamma=0.0001 .............................................
[CV] ................. C=100, gamma=0.0001, score=0.925, total=   0.0s
[CV] C=100, gamma=0.0001 .............................................
[CV] ................. C=100, gamma=0.0001, score=0.912, total=   0.0s
[CV] C=100, gamma=0.0001 .............................................
[CV] ................. C=100, gamma=0.0001, score=0.975, total=   0.0s
[CV] C=100, gamma=0.0001 .............................................
[CV] ................. C=100, gamma=0.0001, score=0.937, total=   0.0s
[CV] C=100, gamma=0.0001 .............................................
[CV] ................. C=100, gamma=0.0001, score=0.949, total=   0.0s
[CV] C=1000, gamma=1 .................................................
[CV] ..................... C=1000, gamma=1, score=0.637, total=   0.0s
[CV] C=1000, gamma=1 .................................................
[CV] ..................... C=1000, gamma=1, score=0.637, total=   0.0s
[CV] C=1000, gamma=1 .................................................
[CV] ..................... C=1000, gamma=1, score=0.625, total=   0.0s
[CV] C=1000, gamma=1 .................................................
[CV] ..................... C=1000, gamma=1, score=0.633, total=   0.0s
[CV] C=1000, gamma=1 .................................................
[CV] ..................... C=1000, gamma=1, score=0.633, total=   0.0s
[CV] C=1000, gamma=0.1 ...............................................
[CV] ................... C=1000, gamma=0.1, score=0.637, total=   0.0s
[CV] C=1000, gamma=0.1 ...............................................
[CV] ................... C=1000, gamma=0.1, score=0.637, total=   0.0s
[CV] C=1000, gamma=0.1 ...............................................
[CV] ................... C=1000, gamma=0.1, score=0.625, total=   0.0s
[CV] C=1000, gamma=0.1 ...............................................
[CV] ................... C=1000, gamma=0.1, score=0.633, total=   0.0s
[CV] C=1000, gamma=0.1 ...............................................
[CV] ................... C=1000, gamma=0.1, score=0.633, total=   0.0s
[CV] C=1000, gamma=0.01 ..............................................
[CV] .................. C=1000, gamma=0.01, score=0.637, total=   0.0s
[CV] C=1000, gamma=0.01 ..............................................
[CV] .................. C=1000, gamma=0.01, score=0.637, total=   0.0s
[CV] C=1000, gamma=0.01 ..............................................
[CV] .................. C=1000, gamma=0.01, score=0.613, total=   0.0s
[CV] C=1000, gamma=0.01 ..............................................
[CV] .................. C=1000, gamma=0.01, score=0.633, total=   0.0s
[CV] C=1000, gamma=0.01 ..............................................
[CV] .................. C=1000, gamma=0.01, score=0.633, total=   0.0s
[CV] C=1000, gamma=0.001 .............................................
[CV] ................. C=1000, gamma=0.001, score=0.887, total=   0.0s
[CV] C=1000, gamma=0.001 .............................................
[CV] ................. C=1000, gamma=0.001, score=0.912, total=   0.0s
[CV] C=1000, gamma=0.001 .............................................
[CV] ................. C=1000, gamma=0.001, score=0.900, total=   0.0s
[CV] C=1000, gamma=0.001 .............................................
[CV] ................. C=1000, gamma=0.001, score=0.937, total=   0.0s
[CV] C=1000, gamma=0.001 .............................................
[CV] ................. C=1000, gamma=0.001, score=0.924, total=   0.0s
[CV] C=1000, gamma=0.0001 ............................................
[CV] ................ C=1000, gamma=0.0001, score=0.938, total=   0.0s
[CV] C=1000, gamma=0.0001 ............................................
[CV] ................ C=1000, gamma=0.0001, score=0.912, total=   0.0s
[CV] C=1000, gamma=0.0001 ............................................
[CV] ................ C=1000, gamma=0.0001, score=0.963, total=   0.0s
[CV] C=1000, gamma=0.0001 ............................................
[CV] ................ C=1000, gamma=0.0001, score=0.924, total=   0.0s
[CV] C=1000, gamma=0.0001 ............................................
[CV] ................ C=1000, gamma=0.0001, score=0.962, total=   0.0s
[Parallel(n_jobs=1)]: Done 125 out of 125 | elapsed:    1.4s finished
Out[37]:
GridSearchCV(estimator=SVC(),
             param_grid={'C': [0.1, 1, 10, 100, 1000],
                         'gamma': [1, 0.1, 0.01, 0.001, 0.0001]},
             verbose=3)
In [38]:
#Show the best params to use
grid.best_params_
Out[38]:
{'C': 1, 'gamma': 0.0001}
In [40]:
#New predictions
grid_predictions = grid.predict(X_test)
In [45]:
#Print out the new confusion matrix and classification reports
print(confusion_matrix(y_test,grid_predictions))
[[ 59   7]
 [  4 101]]
In [46]:
print(classification_report(y_test,grid_predictions))
              precision    recall  f1-score   support

           0       0.94      0.89      0.91        66
           1       0.94      0.96      0.95       105

    accuracy                           0.94       171
   macro avg       0.94      0.93      0.93       171
weighted avg       0.94      0.94      0.94       171

MUCH better - this goes to show you how tuning the model can improve things.