python の future の使い方

import concurrent.futures
import os

score_list = []

def worker(my_random_seed):
    model = CatBoostClassifier(
        iterations=300,
        learning_rate=0.1,
        random_seed=my_random_seed
    )
    model.fit(
        X_train, y_train,
        cat_features=cat_features,
        eval_set=(X_validation, y_validation),
    )
    return model.best_score_['validation_0']['Logloss']

with concurrent.futures.ProcessPoolExecutor(max_workers=os.cpu_count()) as executor:
    futures = {executor.submit(worker, i) for i in range(10)}
    for future in concurrent.futures.as_completed(futures):
        score_list.append(future.result())

機械学習の黄色本 について

機械学習の黄色本

www.amazon.co.jp

https://www.amazon.co.jp/dp/4621061240www.amazon.co.jp

Web PDF

https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf

演習問題 (www) の解答

https://www.microsoft.com/en-us/research/wp-content/uploads/2016/05/prml-web-sol-2009-09-08.pdf

元サイト

www.microsoft.com

t-SNE の有名記事

github.com

Comparison of Manifold Learning methods — scikit-learn 0.20.2 documentation

distill.pub

lvdmaaten.github.io

t-SNE: The effect of various perplexity values on the shape — scikit-learn 0.20.2 documentation

Interaction

Practical Lessons from Predicting Clicks on Ads at Facebook – Facebook Research

Feature transformations with ensembles of trees — scikit-learn 0.20.2 documentation