Machine Learning Day 6

In [1]:

#decision tree can be used as regressor as well as classifier

In [2]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

In [3]:

from sklearn import datasets,metrics
from sklearn.model_selection import train_test_split 
from sklearn.tree import DecisionTreeRegressor

In [4]:

diabetes=datasets.load_diabetes()

In [5]:

print(diabetes.DESCR)

.. _diabetes_dataset:

Diabetes dataset
----------------

Ten baseline variables, age, sex, body mass index, average blood
pressure, and six blood serum measurements were obtained for each of n =
442 diabetes patients, as well as the response of interest, a
quantitative measure of disease progression one year after baseline.

**Data Set Characteristics:**

  :Number of Instances: 442

  :Number of Attributes: First 10 columns are numeric predictive values

  :Target: Column 11 is a quantitative measure of disease progression one year after baseline

  :Attribute Information:
      - Age
      - Sex
      - Body mass index
      - Average blood pressure
      - S1
      - S2
      - S3
      - S4
      - S5
      - S6

Note: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).

Source URL:
https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html

For more information see:
Bradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) "Least Angle Regression," Annals of Statistics (with discussion), 407-499.
(https://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)

In [6]:

x=diabetes.data
y=diabetes.target

In [7]:

df=pd.DataFrame(x,columns=diabetes.feature_names)
df['target']=y
df.head()

Out[7]:

	age	sex	bmi	bp	s1	s2	s3	s4	s5	s6	target
0	0.038076	0.050680	0.061696	0.021872	-0.044223	-0.034821	-0.043401	-0.002592	0.019908	-0.017646	151.0
1	-0.001882	-0.044642	-0.051474	-0.026328	-0.008449	-0.019163	0.074412	-0.039493	-0.068330	-0.092204	75.0
2	0.085299	0.050680	0.044451	-0.005671	-0.045599	-0.034194	-0.032356	-0.002592	0.002864	-0.025930	141.0
3	-0.089063	-0.044642	-0.011595	-0.036656	0.012191	0.024991	-0.036038	0.034309	0.022692	-0.009362	206.0
4	0.005383	-0.044642	-0.036385	0.021872	0.003935	0.015596	0.008142	-0.002592	-0.031991	-0.046641	135.0

In [8]:

x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.20,random_state=101)

In [9]:

regressor=DecisionTreeRegressor(random_state=101)

In [10]:

regressor.fit(x_train,y_train)

Out[10]:

DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,
                      max_leaf_nodes=None, min_impurity_decrease=0.0,
                      min_impurity_split=None, min_samples_leaf=1,
                      min_samples_split=2, min_weight_fraction_leaf=0.0,
                      presort=False, random_state=101, splitter='best')

In [11]:

y_prediction=regressor.predict(x_test)

In [12]:

y_prediction

Out[12]:

array([ 87.,  53., 163., 187., 258., 206., 220.,  88., 109., 158.,  53.,
       198.,  87., 242.,  71., 283., 168., 196., 275.,  53., 265., 141.,
       248., 116.,  68.,  69., 107.,  49., 229., 142.,  83., 181., 142.,
       102.,  93., 128.,  84., 196.,  63., 321.,  66.,  97.,  81., 230.,
       236.,  64., 132., 242.,  91., 233., 146., 190.,  88., 111., 116.,
       151., 200., 198., 147., 172.,  71.,  52.,  49., 297., 198., 135.,
       103.,  90.,  39.,  99., 190., 268., 261., 163., 222., 341., 268.,
       142., 273., 153.,  87.,  71., 150.,  57.,  99.,  53., 221., 135.,
       170.])

In [13]:

new_prediction=regressor.predict([[0.038076,0.050680,0.061696,0.021872,-.044223,-0.034821,-0.043401,-0.002592,0.019908,-0.017646]])

In [14]:

new_prediction

Out[14]:

array([151.])

In [15]:

metrics.mean_squared_error(y_test,y_prediction)

Out[15]:

5124.752808988764

In [17]:

np.sqrt(metrics.mean_squared_error(y_test,y_prediction))

Out[17]:

71.58737883865258

In [18]:

y_test.std()

Out[18]:

79.93591202683353

In [20]:

#now we will use decision tree as classifier

In [21]:

from sklearn.tree import DecisionTreeClassifier

In [22]:

iris=datasets.load_iris()

In [23]:

print(iris.DESCR)

.. _iris_dataset:

Iris plants dataset
--------------------

**Data Set Characteristics:**

    :Number of Instances: 150 (50 in each of three classes)
    :Number of Attributes: 4 numeric, predictive attributes and the class
    :Attribute Information:
        - sepal length in cm
        - sepal width in cm
        - petal length in cm
        - petal width in cm
        - class:
                - Iris-Setosa
                - Iris-Versicolour
                - Iris-Virginica
                
    :Summary Statistics:

    ============== ==== ==== ======= ===== ====================
                    Min  Max   Mean    SD   Class Correlation
    ============== ==== ==== ======= ===== ====================
    sepal length:   4.3  7.9   5.84   0.83    0.7826
    sepal width:    2.0  4.4   3.05   0.43   -0.4194
    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)
    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)
    ============== ==== ==== ======= ===== ====================

    :Missing Attribute Values: None
    :Class Distribution: 33.3% for each of 3 classes.
    :Creator: R.A. Fisher
    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
    :Date: July, 1988

The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken
from Fisher's paper. Note that it's the same as in R, but not as in the UCI
Machine Learning Repository, which has two wrong data points.

This is perhaps the best known database to be found in the
pattern recognition literature.  Fisher's paper is a classic in the field and
is referenced frequently to this day.  (See Duda & Hart, for example.)  The
data set contains 3 classes of 50 instances each, where each class refers to a
type of iris plant.  One class is linearly separable from the other 2; the
latter are NOT linearly separable from each other.

.. topic:: References

   - Fisher, R.A. "The use of multiple measurements in taxonomic problems"
     Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
     Mathematical Statistics" (John Wiley, NY, 1950).
   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.
     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
   - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
     Structure and Classification Rule for Recognition in Partially Exposed
     Environments".  IEEE Transactions on Pattern Analysis and Machine
     Intelligence, Vol. PAMI-2, No. 1, 67-71.
   - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions
     on Information Theory, May 1972, 431-433.
   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II
     conceptual clustering system finds 3 classes in the data.
   - Many, many more ...

In [24]:

x=iris.data
y=iris.target

In [25]:

df1=pd.DataFrame(x,columns=iris.feature_names)
df1['target']=y
df1.head()

Out[25]:

	sepal length (cm)	sepal width (cm)	petal length (cm)	petal width (cm)	target
0	5.1	3.5	1.4	0.2	0
1	4.9	3.0	1.4	0.2	0
2	4.7	3.2	1.3	0.2	0
3	4.6	3.1	1.5	0.2	0
4	5.0	3.6	1.4	0.2	0

In [26]:

x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.20,random_state=101)

In [27]:

clf=DecisionTreeClassifier(random_state=101)

In [28]:

clf.fit(x_train,y_train)

Out[28]:

DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
                       max_features=None, max_leaf_nodes=None,
                       min_impurity_decrease=0.0, min_impurity_split=None,
                       min_samples_leaf=1, min_samples_split=2,
                       min_weight_fraction_leaf=0.0, presort=False,
                       random_state=101, splitter='best')

In [29]:

y_pred=clf.predict(x_test)

In [30]:

metrics.accuracy_score(y_test,y_pred)

Out[30]:

0.9666666666666667

In [31]:

metrics.confusion_matrix(y_test,y_pred)

Out[31]:

array([[10,  0,  0],
       [ 0, 12,  0],
       [ 0,  1,  7]], dtype=int64)

In [32]:

y_pred

Out[32]:

array([0, 0, 0, 1, 1, 2, 1, 1, 2, 0, 2, 0, 0, 2, 2, 1, 1, 1, 0, 2, 1, 0,
       1, 1, 1, 1, 1, 2, 0, 0])

In [ ]: