sklearn python

Install sklearn (http://scikit-learn.org/stable/install.html)

Better to update pip first

In win10, “in left bottom corner window: Type here to search”, type “cmd” , you will see cmd coming up, select admin mode by right click cmd to select the admin mode.

Scikit-learn requires:

· Python (>= 2.7 or >= 3.3),

· NumPy (>= 1.8.2),

· SciPy (>= 0.13.3).

>python -m pip install --upgrade pip

>pip install --upgrade numpy

>pip install --upgrade scipy

>pip install -U scikit-learn

Done

Now run IDLE(python)

See an run http://scikit-learn.org/stable/tutorial/basic/tutorial.html

Or run

http://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html#sphx-glr-auto-examples-classification-plot-digits-classification-py

May also install winpython:

https://sourceforge.net/projects/winpython/files/WinPython_3.6/3.6.6.1/WinPython32-3.6.6.1Qt5.exe/download

Demo on different topics https://scikit-learn.org/stable/user_guide.html

• 1. bagging https://scikit-learn.org/stable/modules/ensemble.html#bagging-meta-estimator

  2. clustering https://scikit-learn.org/stable/modules/clustering.html#overview-of-clustering-methods

  3. Neural net https://scikit-learn.org/stable/modules/neural_networks_unsupervised.html

  4. ensemble methods https://scikit-learn.org/stable/modules/ensemble.html

  5. Kernal density https://scikit-learn.org/stable/modules/density.html#kernel-density-estimation

  6. working with text https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html#loading-the-20-newsgroups-dataset

Tested:

• 1. Iris dataset https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html#sphx-glr-auto-examples-datasets-plot-iris-dataset-py

  2. Digit recognition https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html#sphx-glr-auto-examples-classification-plot-digits-classification-py

  3. SVM/PCA face recognition https://scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html#sphx-glr-auto-examples-applications-plot-face-recognition-py

  4. Decision Tree https://scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html#sphx-glr-auto-examples-tree-plot-iris-dtc-py

  5. Decision Tree https://scikit-learn.org/stable/auto_examples/tree/plot_tree_regression.html#sphx-glr-auto-examples-tree-plot-tree-regression-py

  6. Species distribution modeling https://scikit-learn.org/stable/auto_examples/applications/plot_species_distribution_modeling.html#sphx-glr-auto-examples-applications-plot-species-distribution-modeling-py

  7. kmeans https://scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html#sphx-glr-auto-examples-applications-plot-face-recognition-py

  8. Decision tree and adaboost https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_regression.html#sphx-glr-auto-examples-ensemble-plot-adaboost-regression-py

1. Feature importances with forests of trees https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html#feature-importances-with-forests-of-trees

   1. Comparing random forests and the multi-output meta estimator https://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py

   2. Two-class AdaBoost https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_twoclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-twoclass-py

   3. Gaussian mixture https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_sin.html#sphx-glr-auto-examples-mixture-plot-gmm-sin-py

   4. Classification of text documents using sparse features https://scikit-learn.org/stable/auto_examples/text/plot_document_classification_20newsgroups.html#sphx-glr-auto-examples-text-plot-document-classification-20newsgroups-py

   5. Working with text document working_with_text_data.py (see below) https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html#loading-the-20-newsgroups-dataset

      1. word vector representation https://medium.com/@japneet121/introduction-713b3d976323

   6. https://en.wikipedia.org/wiki/N-gram

• • 1. Countervectorizer https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html

    2. word2vec https://en.wikipedia.org/wiki/Word2vec