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…

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 h…

Kaggle Ensembling Guide | MLWave github.com github.com

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 Le…

www.dataquest.io おまけ kaggletils github.com

アプリケーションは基本 container で立ち上げる前提のOS っぽい。便利そうだけど、複雑。 https://coreos.com/ignition/docs/latest/what-is-ignition.html

mysql> CREATE DATABASE `test` DEFAULT CHARSET utf8mb4; ERROR 1290 (HY000): The MySQL server is running with the --super-read-only option so it cannot execute this statement SET GLOBAL super_read_only= 0;

$ docker exec -it jenkins env COLUMNS=200 LINES=50 TERM=xterm bash qiita.com

パート I. ファイルシステム - Red Hat Customer Portal

3.2. Tuning the hyper-parameters of an estimator — scikit-learn 0.20.1 documentation fastml.com www.analyticsvidhya.com

import pandas as pd import numpy as np index_cols = ['shop_id', 'item_id', 'cnt'] global_mean = 0.2 df = pd.read_csv(filename) # groupby した gb = df.groupby(index_cols,as_index=False).agg({'cnt':{'target':'sum'}}) #fix column names gb.col…

KILLED のプロセスが transaction 掴んで焦った話。KILLED を消すためにmysqldを強制終了すると、dead lock が発生するのでやめたほうがいい。 以下のメッセージが出て追加deleteができなかった。。 transaction mysql Lock wait timeout exceeded; try rest…

rank https://icml.cc/2015/wp-content/uploads/2015/06/icml_ranking.pdf https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/MSR-TR-2010-82.pdf The Lemur Project / Wiki / RankLib Learning to Rank Overviewwellecks.wordpress.com…

Binary Class の測定 logloss l_pred = [0.5, 0.5, 0.5, 0.5] l_label = [0, 0, 0, 0] def logloss(l_pred, l_label): n = len(l_pred) score = 0 for t in range(n): i = l_pred[t] k = l_label[t] score += k * np.log(i) + (1 - k) * np.log(i) return - …