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![7
Data Partitioning
• Data and labels
–{[data], [labels]}
–{[3,7, 76, 11, 22, 37, 56,2],[T, T, F, T, F, F, F, T]}
–Data: [Age, Do you love Nutella?]
• Partitioning will create
–{[train data], [train labels],[test data], [test labels]}
–We usually split the data on a ration of 9:1
–There is a tradeoff between the effectiveness of
the test and the learning we could provide to the
classifier
• We will look at a partitioning function later](https://image.slidesharecdn.com/introtomachinelearningwithscikit-learn-150623073429-lva1-app6891/75/Intro-to-machine-learning-with-scikit-learn-7-2048.jpg)
![20
Iris Data
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features.
Y = iris.target
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5](https://image.slidesharecdn.com/introtomachinelearningwithscikit-learn-150623073429-lva1-app6891/75/Intro-to-machine-learning-with-scikit-learn-20-2048.jpg)
![21
Plot Iris Data
plt.figure(2, figsize=(8, 6))
plt.clf()
plt.scatter(X[:, 0], X[:, 1],
c=Y, cmap=plt.cm.Paired)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xticks(())
plt.yticks(())](https://image.slidesharecdn.com/introtomachinelearningwithscikit-learn-150623073429-lva1-app6891/75/Intro-to-machine-learning-with-scikit-learn-21-2048.jpg)
![22
Add PCA for better classification
fig = plt.figure(1, figsize=(8, 6))
ax = Axes3D(fig, elev=-150, azim=110)
X_reduced = PCA(n_components=3).fit_transform(iris.data)
ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=Y,
cmap=plt.cm.Paired)
ax.set_title("First three PCA directions")
ax.set_xlabel("1st eigenvector")
ax.w_xaxis.set_ticklabels([])
ax.set_ylabel("2nd eigenvector")
ax.w_yaxis.set_ticklabels([])
ax.set_zlabel("3rd eigenvector")
ax.w_zaxis.set_ticklabels([])
plt.show()](https://image.slidesharecdn.com/introtomachinelearningwithscikit-learn-150623073429-lva1-app6891/75/Intro-to-machine-learning-with-scikit-learn-22-2048.jpg)
![7
Data Partitioning
• Data and labels
–{[data], [labels]}
–{[3,7, 76, 11, 22, 37, 56,2],[T, T, F, T, F, F, F, T]}
–Data: [Age, Do you love Nutella?]
• Partitioning will create
–{[train data], [train labels],[test data], [test labels]}
–We usually split the data on a ration of 9:1
–There is a tradeoff between the effectiveness of
the test and the learning we could provide to the
classifier
• We will look at a partitioning function later](https://crownmelresort.com/image.slidesharecdn.com/introtomachinelearningwithscikit-learn-150623073429-lva1-app6891/75/Intro-to-machine-learning-with-scikit-learn-7-2048.jpg)
![20
Iris Data
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features.
Y = iris.target
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5](https://crownmelresort.com/image.slidesharecdn.com/introtomachinelearningwithscikit-learn-150623073429-lva1-app6891/75/Intro-to-machine-learning-with-scikit-learn-20-2048.jpg)
![21
Plot Iris Data
plt.figure(2, figsize=(8, 6))
plt.clf()
plt.scatter(X[:, 0], X[:, 1],
c=Y, cmap=plt.cm.Paired)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xticks(())
plt.yticks(())](https://crownmelresort.com/image.slidesharecdn.com/introtomachinelearningwithscikit-learn-150623073429-lva1-app6891/75/Intro-to-machine-learning-with-scikit-learn-21-2048.jpg)
![22
Add PCA for better classification
fig = plt.figure(1, figsize=(8, 6))
ax = Axes3D(fig, elev=-150, azim=110)
X_reduced = PCA(n_components=3).fit_transform(iris.data)
ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=Y,
cmap=plt.cm.Paired)
ax.set_title("First three PCA directions")
ax.set_xlabel("1st eigenvector")
ax.w_xaxis.set_ticklabels([])
ax.set_ylabel("2nd eigenvector")
ax.w_yaxis.set_ticklabels([])
ax.set_zlabel("3rd eigenvector")
ax.w_zaxis.set_ticklabels([])
plt.show()](https://crownmelresort.com/image.slidesharecdn.com/introtomachinelearningwithscikit-learn-150623073429-lva1-app6891/75/Intro-to-machine-learning-with-scikit-learn-22-2048.jpg)
Uploaded byYoss Cohen
Intro to machine learning with scikit learn
The document discusses machine learning concepts and programming with scikit-learn. It introduces the machine learning process of getting data, pre-processing, partitioning for training and testing, creating a classifier, training and evaluating the model. As an example, it loads the Iris dataset and plots sepal length vs width with labels. It also uses PCA for dimensionality reduction to better classify the Iris data in 3 dimensions.
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Intro to machine learning with scikit learn
- 1.
- 2. 2 INTRO TO MLPROGRAMMING
- 3. 3 ML Programming 1. GetData Get labels for supervised learning 1. Create a classifier 2. Train the classifier 3. Predict test data 4. Evaluate predictor accuracy *Configure and improve by repeating 2-5
- 4.
- 5. 5 Get Data &Labels • Sources –Open data sources –Collect on your own • Verify data validity and correctness • Wrangle data –make it readable by computer –Filter it • Remove Outliers PANDAS Python library could assist in pre- processing & data manipulation before ML http://pandas.pydata.org/
- 6. 6 Pre-Processing Change formatting Remove redundantdata Filter Data (take partial data) Remove Outliers Label Split for testing (10/90, 20/80)
- 7. 7 Data Partitioning • Dataand labels –{[data], [labels]} –{[3,7, 76, 11, 22, 37, 56,2],[T, T, F, T, F, F, F, T]} –Data: [Age, Do you love Nutella?] • Partitioning will create –{[train data], [train labels],[test data], [test labels]} –We usually split the data on a ration of 9:1 –There is a tradeoff between the effectiveness of the test and the learning we could provide to the classifier • We will look at a partitioning function later
- 8. 8 Learn (The “SmartPart”) Classification If the output is discrete to a limited amount of classes (groups) Regression If the output is continues
- 9.
- 10. 10 Create Classifier For mostSUPERVISED LEARNING algorithms this would be C = ClassifyAlg(Params) Its up to us (ML guys) to set the best params How? 1. We could develop a hunch for it 2. Perform an exhaustive search
- 11. 11 Train the classifier Weassigned C = ClassifyAlg(Params) This is a general algorithm with some initalizer and configurations. In this stage we train it using: C.fit(Data, Labels)
- 12. 12 Predict After we havea trained Algorithm classifier C Prdeicted_Labels = C.predict(Data)
- 13. 13 Predictor Evaluation We arenot done yet There is a need to evaluate the predictor accuracy in comparison to other predictors and to the system requirements We will learn several methods for this
- 14.
- 15. 15 The Environment • Thereare many existing environments and tools we could use –Matlab with Machine learning toolbox –Apache Mahout –Python with Scikit-learn • Additional tools –Hadoop / Map-Reduce to accelerate and parallelize large data set processing –Amazon ML tools –NVIDIA Tools
- 16. 16 Scikit-learn • Installation Instructionsin http://scikit-learn.org/stable/install.html#install-official-release • Depends on two other libraries • numpy and scipy • Easiest way to install on windows: • Install WinPython http://sourceforge.net/projects/winpython/files/WinPython_2.7/2.7.9.4/ –Lets install this together For Linux / Mac computers just install the 3 libs separately using PIP
- 17.
- 18. 18 Data sets There aremany data sets to work on One of them is the Iris data classification into three groups. It has an interesting story you could google later Well work on the iris data
- 19. 19 Lab A –Plot the Iris data Plot septal length vs septal width with labels ONLY How? Google Iris data and the scikit learn environment Try to understand the second part of the program with the PCA
- 20. 20 Iris Data import matplotlib.pyplotas plt from mpl_toolkits.mplot3d import Axes3D from sklearn import datasets iris = datasets.load_iris() X = iris.data[:, :2] # we only take the first two features. Y = iris.target x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5 y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
- 21. 21 Plot Iris Data plt.figure(2,figsize=(8, 6)) plt.clf() plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired) plt.xlabel('Sepal length') plt.ylabel('Sepal width') plt.xlim(x_min, x_max) plt.ylim(y_min, y_max) plt.xticks(()) plt.yticks(())
- 22. 22 Add PCA forbetter classification fig = plt.figure(1, figsize=(8, 6)) ax = Axes3D(fig, elev=-150, azim=110) X_reduced = PCA(n_components=3).fit_transform(iris.data) ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=Y, cmap=plt.cm.Paired) ax.set_title("First three PCA directions") ax.set_xlabel("1st eigenvector") ax.w_xaxis.set_ticklabels([]) ax.set_ylabel("2nd eigenvector") ax.w_yaxis.set_ticklabels([]) ax.set_zlabel("3rd eigenvector") ax.w_zaxis.set_ticklabels([]) plt.show()
- 23.
- 24.
- 25. 25 Thank you! More Aboutme: Yossi CohenYossi Cohen yossicohen19@gmail.comyossicohen19@gmail.com +972-545-313092+972-545-313092 Video compression and computer vision enthusiast & lecturer Surfer