(2順目)
レシピ8.7
2項
scikit-learnが持っているdatasetを使用する。
print(iris['DESCR'])でデータセット概要をプリントアウト
Iris Plants Database
Notes
-----
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:
データ数は150個あって、その特徴量は4つあって、sepal length(がく片長)、sepal width(がく片幅)、petal length(花びら長)、petal width(花びら幅)
============== ==== ==== ======= ===== ====================
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
This is a copy of UCI ML iris datasets.
http://archive.ics.uci.edu/ml/datasets/Iris
The famous Iris database, first used by Sir R.A Fisher
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.
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 ...
3項
最初のがく片長とがく片幅をplotする
classは3つあって
Iris-Setosa
Iris-Versicolour
Iris-Virginica
Setosa
出典:https://en.wikipedia.org/wiki/Iris_setosa
Versicolour
出典:https://en.wikipedia.org/wiki/Iris_versicolor
Virginica
出典:https://en.wikipedia.org/wiki/Iris_virginica
4,5項
品種の違いがわかるようになった。
6項
他のPCAも試してみる。まず「SparsePCA」
X_ter = dec.SparsePCA().fit_transform(X)
plt.figure(figsize=(6,3));
plt.scatter(X_ter[:,0], X_ter[:,1], c=y, s=30, cmap=plt.cm.rainbow);
次は「RandomizedPCA」
X_ter = dec.RandomizedPCA().fit_transform(X)
plt.figure(figsize=(6,3));
plt.scatter(X_ter[:,0], X_ter[:,1], c=y, s=30, cmap=plt.cm.rainbow);