# ML - Iris dataset
###### tags: `machine learning`
### Basic concept in ML
Supervised Learning
- Supervised learning has two major problem group, "classification" and "regression".
- Goal of supervised learning in classification problem is to predict "class label". It can be binary classification or multi-class classification.
- Goal of supervised learning in regression problem is to predict a continuously value. For example, to predict how much income.
Generalize
- If a model has accurately prediction to a new data-set, then this model means it can generalize from training set to testing set.
- If a mode detailed focused in training set, have a good prediction in training set, but it does not generalize to new data set, then this mode is "overfitting".
- In contrary, if a model is too simple, it does not capture all data set content and changes in the data set, or it has poor prediction in training set. This mode is "underfitting".
Regularization
- In general, regularization means to make things regular or acceptable.
- Explicitly restricting a model to avoid overfitting.
- A technique used for tuning a function by adding additional penalty term in error function.
### Understand Iris data set
```
#!/usr/bin/env python
from sklearn.datasets import load_iris
iris_dataset = load_iris()
print("keys of iris_dataset:\n{}".format(iris_dataset.keys()))
print(iris_dataset['DESCR'])
print("Target names: {}".format(iris_dataset['target_names']))
print("Feature names:\n{}".format(iris_dataset['feature_names']))
print("Type of data: {}".format(type(iris_dataset['data'])))
print("Shape of data: {}".format(iris_dataset['data'].shape))
print("First five rows of data:\n{}".format(iris_dataset['data'][:5]))
print("Type of target: {}".format(type(iris_dataset['target'])))
print("Shape of target: {}".format(iris_dataset['target'].shape))
print("Target:\n{}".format(iris_dataset['target']))
```
#### output
```
keys of iris_dataset: dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])
.. _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 ...
Target names: ['setosa' 'versicolor' 'virginica']
Feature names:
['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
Type of data: <class 'numpy.ndarray'>
Shape of data: (150, 4)
First five rows of data:
[[5.1 3.5 1.4 0.2]
[4.9 3. 1.4 0.2]
[4.7 3.2 1.3 0.2]
[4.6 3.1 1.5 0.2]
[5. 3.6 1.4 0.2]]
Type of target:
Shape of target: (150,)
Target:
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 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 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2]
```
### evaluate iris dataset
```
import mglearn
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(iris_dataset['data'], iris_dataset['target'], random_state=0)
print("X_train shape: {}".format(X_train.shape))
print("y_train shape: {}".format(y_train.shape))
print("X_test shape: {}".format(X_test.shape))
print("y_test shape: {}".format(y_test.shape))
iris_dataframe = pd.DataFrame(X_train, columns=iris_dataset.feature_names)
grr = pd.plotting.scatter_matrix(iris_dataframe,
c=y_train,
figsize=(10, 10),
marker='0',
hist_kwds={'bins': 20},
s=60,
alpha=.8,
cmap=mglearn.cm3)
plt.show()
```
#### output
```
X_train shape: (112, 4)
y_train shape: (112,)
X_test shape: (38, 4)
y_test shape: (38,)
```
### use KNN to train iris dataset
```
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=1)
print(knn.fit(X_train, y_train))
X_new = np.array([[5, 2.9, 1, 0.2]])
print("X_new.shape: {}".format(X_new.shape))
prediction = knn.predict(X_new)
print("Prediction: {}".format(prediction))
print("Predicted target name: {}".format(iris_dataset['target_names'][prediction]))
y_pred = knn.predict(X_test)
print("Test set predictions:\n {}".format(y_pred))
print("Test set score: {:.2f}".format(np.mean(y_pred == y_test)))
print("Test set score: {:.2f}".format(knn.score(X_test, y_test)))
```
#### output
```
KNeighborsClassifier(n_neighbors=1)
X_new.shape: (1, 4)
Prediction: [0]
Predicted target name: ['setosa']
Test set predictions:
[2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0
2]
Test set score: 0.97
Test set score: 0.97
```
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