Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
How to use scikit-learn to solve machine learni...
Search
Olivier Grisel
April 22, 2015
Technology
0
930
How to use scikit-learn to solve machine learning problems
AutoML Hackathon - Paris - April 2015
Olivier Grisel
April 22, 2015
Tweet
Share
More Decks by Olivier Grisel
See All by Olivier Grisel
Intro to scikit-learn
ogrisel
5
660
An Intro to Deep Learning
ogrisel
1
230
Predictive Modeling and Deep Learning
ogrisel
2
340
Intro to scikit-learn and what's new in 0.17
ogrisel
1
300
Big Data, Predictive Modeling and tools
ogrisel
2
240
Recent Developments in Deep Learning
ogrisel
3
650
Documentation
ogrisel
2
180
Build and test wheel packages on Linux, OSX and Windows
ogrisel
2
320
Big Data and Predictive Modeling
ogrisel
3
220
Other Decks in Technology
See All in Technology
App Router を実プロダクトで採用して見えてきた勘所をちょっとだけ紹介
marokanatani
1
930
PDF Viewer作成の今までとこれから
hunachi
0
470
OR学会2024秋_短期収益と将来のオフ方策評価性能を考慮したクーポン割当方策混合比の決定
recruitengineers
PRO
4
460
不動産 x AIことはじめ~データの真価を拓くために
estie
0
110
あなたの知らないiOS開発の世界
recruitengineers
PRO
3
180
とあるOSSを継続可能にするための取り組みについて / OSS Refactoring Process
bun913
1
210
Swift Testingのconfirmationを コードリーディング/Dive into Swift Testing confirmation
laprasdrum
2
260
Cloud Run と GitHub Template Repository による軽量なアプリケーションプラットフォーム/ #nikkei_tech_talk
nikkei_engineer_recruiting
0
110
サーバレスでモバイルアプリ開発! NTTコム「ビジネスdアプリ」のアーキテクチャ / The architecture of business d app
nttcom
12
240
効果的なオンコール対応と障害対応
ryuichi1208
6
3.1k
「自動テストのプラクティスを効果的に学ぶためのカードゲーム」 ( #sqip2024 )
teyamagu
PRO
2
180
React Aria で実現する次世代のアクセシビリティ
ryo_manba
4
1.2k
Featured
See All Featured
Become a Pro
speakerdeck
PRO
22
4.9k
Happy Clients
brianwarren
96
6.6k
Keith and Marios Guide to Fast Websites
keithpitt
408
22k
Music & Morning Musume
bryan
46
6k
The Invisible Customer
myddelton
119
13k
Agile that works and the tools we love
rasmusluckow
327
20k
Gamification - CAS2011
davidbonilla
79
5k
Cheating the UX When There Is Nothing More to Optimize - PixelPioneers
stephaniewalter
278
13k
A Modern Web Designer's Workflow
chriscoyier
691
190k
10 Git Anti Patterns You Should be Aware of
lemiorhan
653
58k
Creating an realtime collaboration tool: Agile Flush - .NET Oxford
marcduiker
23
1.7k
The Pragmatic Product Professional
lauravandoore
31
6.2k
Transcript
How to use scikit-learn to solve machine learning problems AutoML
Hackathon April 2015
Outline • Machine Learning refresher • scikit-learn • Demo: interactive
predictive modeling on Census Data with IPython notebook / pandas / scikit-learn • Combining models with Pipeline and parameter search
Predictive modeling ~= machine learning • Make predictions of outcome
on new data • Extract the structure of historical data • Statistical tools to summarize the training data into a executable predictive model • Alternative to hard-coded rules written by experts
type (category) # rooms (int) surface (float m2) public trans
(boolean) Apartment 3 50 TRUE House 5 254 FALSE Duplex 4 68 TRUE Apartment 2 32 TRUE
type (category) # rooms (int) surface (float m2) public trans
(boolean) Apartment 3 50 TRUE House 5 254 FALSE Duplex 4 68 TRUE Apartment 2 32 TRUE sold (float k€) 450 430 712 234
type (category) # rooms (int) surface (float m2) public trans
(boolean) Apartment 3 50 TRUE House 5 254 FALSE Duplex 4 68 TRUE Apartment 2 32 TRUE sold (float k€) 450 430 712 234 features target samples (train)
type (category) # rooms (int) surface (float m2) public trans
(boolean) Apartment 3 50 TRUE House 5 254 FALSE Duplex 4 68 TRUE Apartment 2 32 TRUE sold (float k€) 450 430 712 234 features target samples (train) Apartment 2 33 TRUE House 4 210 TRUE samples (test) ? ?
Training text docs images sounds transactions Labels Machine Learning Algorithm
Model Predictive Modeling Data Flow Feature vectors
New text doc image sound transaction Model Expected Label Predictive
Modeling Data Flow Feature vector Training text docs images sounds transactions Labels Machine Learning Algorithm Feature vectors
Inventory forecasting & trends detection Predictive modeling in the wild
Personalized radios Fraud detection Virality and readers engagement Predictive maintenance Personality matching
• Library of Machine Learning algorithms • Focus on established
methods (e.g. ESL-II) • Open Source (BSD) • Simple fit / predict / transform API • Python / NumPy / SciPy / Cython • Model Assessment, Selection & Ensembles
Train data Train labels Model Fitted model Test data Predicted
labels Test labels Evaluation model = ModelClass(**hyperparams) model.fit(X_train, y_train)
Train data Train labels Model Fitted model Test data Predicted
labels Test labels Evaluation model = ModelClass(**hyperparams) model.fit(X_train, y_train) y_pred = model.predict(X_test)
Train data Train labels Model Fitted model Test data Predicted
labels Test labels Evaluation model = ModelClass(**hyperparams) model.fit(X_train, y_train) y_pred = model.predict(X_test) accuracy_score(y_test, y_pred)
Support Vector Machine from sklearn.svm import SVC model = SVC(kernel="rbf",
C=1.0, gamma=1e-4) model.fit(X_train, y_train) y_predicted = model.predict(X_test) from sklearn.metrics import f1_score f1_score(y_test, y_predicted)
Linear Classifier from sklearn.linear_model import SGDClassifier model = SGDClassifier(alpha=1e-4, penalty="elasticnet")
model.fit(X_train, y_train) y_predicted = model.predict(X_test) from sklearn.metrics import f1_score f1_score(y_test, y_predicted)
Random Forests from sklearn.ensemble import RandomForestClassifier model = RandomForestClassifier(n_estimators=200) model.fit(X_train,
y_train) y_predicted = model.predict(X_test) from sklearn.metrics import f1_score f1_score(y_test, y_predicted)
None
None
Demo time! http://nbviewer.ipython.org/github/ogrisel/notebooks/blob/ master/sklearn_demos/Income%20classification.ipynb https://github.com/ogrisel/notebooks
Combining Models from sklearn.preprocessing import StandardScaler from sklearn.decomposition import RandomizedPCA
from sklearn.svm import SVC scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train) pca = RandomizedPCA(n_components=10) X_train_pca = pca.fit_transform(X_train_scaled) svm = SVC(C=0.1, gamma=1e-3) svm.fit(X_train_pca, y_train)
Pipeline from sklearn.preprocessing import StandardScaler from sklearn.decomposition import RandomizedPCA from
sklearn.svm import SVC from sklearn.pipeline import make_pipeline pipeline = make_pipeline( StandardScaler(), RandomizedPCA(n_components=10), SVC(C=0.1, gamma=1e-3), ) pipeline.fit(X_train, y_train)
Scoring manually stacked models scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train)
pca = RandomizedPCA(n_components=10) X_train_pca = pca.fit_transform(X_train_scaled) svm = SVC(C=0.1, gamma=1e-3) svm.fit(X_train_pca, y_train) X_test_scaled = scaler.transform(X_test) X_test_pca = pca.transform(X_test_scaled) y_pred = svm.predict(X_test_pca) accuracy_score(y_test, y_pred)
Scoring a pipeline pipeline = make_pipeline( RandomizedPCA(n_components=10), SVC(C=0.1, gamma=1e-3), )
pipeline.fit(X_train, y_train) y_pred = pipeline.predict(X_test) accuracy_score(y_test, y_pred)
Parameter search import numpy as np from sklearn.grid_search import RandomizedSearchCV
params = { 'randomizedpca__n_components': [5, 10, 20], 'svc__C': np.logspace(-3, 3, 7), 'svc__gamma': np.logspace(-6, 0, 7), } search = RandomizedSearchCV(pipeline, params, n_iter=30, cv=5) search.fit(X_train, y_train) # search.best_params_, search.grid_scores_
Thank you! • http://scikit-learn.org • https://github.com/scikit-learn/scikit-learn @ogrisel