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
1k
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
690
An Intro to Deep Learning
ogrisel
1
270
Predictive Modeling and Deep Learning
ogrisel
2
350
Intro to scikit-learn and what's new in 0.17
ogrisel
1
350
Big Data, Predictive Modeling and tools
ogrisel
2
280
Recent Developments in Deep Learning
ogrisel
3
670
Documentation
ogrisel
2
240
Build and test wheel packages on Linux, OSX and Windows
ogrisel
2
330
Big Data and Predictive Modeling
ogrisel
3
230
Other Decks in Technology
See All in Technology
現場が抱える様々な問題は “組織設計上” の問題によって生じていることがある / Team-oriented Organization Design 20250827
mtx2s
5
1.1k
Devinを使ったモバイルアプリ開発 / Mobile app development with Devin
yanzm
0
190
JuniorからSeniorまで: DevOpsエンジニアの成長ロードマップ
yuriemori
0
190
ECS モニタリング手法大整理
yendoooo
1
120
新卒(ほぼ)専業Kagglerという選択肢
nocchi1
1
2.3k
夢の印税生活 / Life on Royalties
tmtms
0
280
Gaze-LLE: Gaze Target Estimation via Large-Scale Learned Encoders
kzykmyzw
0
320
新規案件の立ち上げ専門チームから見たAI駆動開発の始め方
shuyakinjo
0
120
浸透しなさいRFC 5322&7208
hinono
0
120
退屈なことはDevinにやらせよう〜〜Devin APIを使ったVisual Regression Testの自動追加〜
kawamataryo
3
630
モダンな現場と従来型の組織——そこに生じる "不整合" を解消してこそチームがパフォーマンスを発揮できる / Team-oriented Organization Design 20250825
mtx2s
6
550
AIエージェント就活入門 - MCPが履歴書になる未来
eltociear
0
510
Featured
See All Featured
ReactJS: Keep Simple. Everything can be a component!
pedronauck
667
120k
Visualization
eitanlees
147
16k
Fantastic passwords and where to find them - at NoRuKo
philnash
51
3.4k
4 Signs Your Business is Dying
shpigford
184
22k
Principles of Awesome APIs and How to Build Them.
keavy
126
17k
RailsConf 2023
tenderlove
30
1.2k
Automating Front-end Workflow
addyosmani
1370
200k
[RailsConf 2023 Opening Keynote] The Magic of Rails
eileencodes
30
9.6k
Unsuck your backbone
ammeep
671
58k
RailsConf & Balkan Ruby 2019: The Past, Present, and Future of Rails at GitHub
eileencodes
139
34k
Scaling GitHub
holman
462
140k
Building an army of robots
kneath
306
46k
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