投稿時間:2023-01-26 15:15:33 RSSフィード2023-01-26 15:00 分まとめ(17件)

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IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 働く女性の5割が「ボーナスなし」、理想の支給額は？	https://www.itmedia.co.jp/business/articles/2301/26/news130.html	itmedia	2023-01-26 14:53:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 就活生向け「無料プログラミング研修」開始、就職後のミスマッチ防ぐ狙いも　ネオキャリア	https://www.itmedia.co.jp/business/articles/2301/26/news128.html	itmedia	2023-01-26 14:42:00
IT	ITmedia 総合記事一覧	[ITmedia PC USER] Samsung、ポータブルSSD「T7 Shield」に4TBモデルを追加	https://www.itmedia.co.jp/pcuser/articles/2301/26/news137.html	itmediapcusersamsung	2023-01-26 14:38:00
IT	ITmedia 総合記事一覧	[ITmedia News] 「Starlink」活用のドローンが秩父市を救う？　土砂崩れで止まった物流を空で支援、定期配送スタート	https://www.itmedia.co.jp/news/articles/2301/26/news138.html	itmedia	2023-01-26 14:30:00
IT	ITmedia 総合記事一覧	[ITmedia News] アナログレコード生産額、33年ぶり40億円超え　2年で倍に	https://www.itmedia.co.jp/news/articles/2301/26/news136.html	itmedia	2023-01-26 14:24:00
AWS	AWS Japan Blog	新規開設 – オーストラリアの AWS アジアパシフィック (メルボルン) リージョン	https://aws.amazon.com/jp/blogs/news/now-open-aws-asia-pacific-melbourne-region-in-australia/	開設	2023-01-26 05:03:28
python	Pythonタグが付けられた新着投稿 - Qiita	EPGStation で WebAPI を使ってエンコード自動化	https://qiita.com/nanbuwks/items/ccdf030a32e0be25db2a	epgstation	2023-01-26 14:39:36
python	Pythonタグが付けられた新着投稿 - Qiita	Windowsでigraphを入れてpythonから使うまで	https://qiita.com/melonsode/items/f63fa338a89ff1b3b195	igraph	2023-01-26 14:29:27
js	JavaScriptタグが付けられた新着投稿 - Qiita	youtube動画を検索するJavaScript	https://qiita.com/sueasen/items/13c0033f3137fee397c5	youtubeda	2023-01-26 14:20:50
Linux	Ubuntuタグが付けられた新着投稿 - Qiita	EPGStation で WebAPI を使ってエンコード自動化	https://qiita.com/nanbuwks/items/ccdf030a32e0be25db2a	epgstation	2023-01-26 14:39:36
Docker	dockerタグが付けられた新着投稿 - Qiita	Steam Deckは開発環境になるのか	https://qiita.com/wakin/items/bfcc019cc5a3353d14ae	docker	2023-01-26 14:54:51
技術ブログ	Developers.IO	EventBridgeスケジューラを使ってEC2の定期起動/停止を行う方法	https://dev.classmethod.jp/articles/ec2-schedule-using-eventbridge-scheduler/	eventbridge	2023-01-26 05:32:08
海外TECH	DEV Community	Bias vs Variance: The Key to Successful Predictive Modeling	https://dev.to/anurag629/bias-vs-variance-the-key-to-successful-predictive-modeling-mbp	Bias vs Variance The Key to Successful Predictive ModelingAs a machine learning and data science enthusiast you ve probably heard the terms bias and variance thrown around quite a bit But what do these terms actually mean and why are they so important In this post we ll take a closer look at bias and variance and discuss how to balance them for optimal performance in your models Bias refers to the difference between the predicted values of a model and the true values of the data In simpler terms it s the degree to which a model s predictions are consistently incorrect For example imagine you re trying to predict the price of a car based on its features A model with high bias might always predict the price to be lower than it actually is regardless of the specific car On the other hand variance refers to the variability of a model s predictions for different training sets In other words it s the degree to which a model s predictions change depending on the specific data it s trained on For example imagine you re using the same car price prediction model but you train it on two different datasets A model with high variance might give you very different predictions for the same car depending on which dataset it was trained on So why is it important to balance bias and variance A model with high bias and low variance is said to be underfitting the data meaning it s not capturing the complexity of the relationship between the input and output variables On the other hand a model with low bias and high variance is said to be overfitting the data meaning it s fitting the noise in the training data rather than the underlying pattern The goal is to find a model that has a good balance of bias and variance known as good fit There are several techniques you can use to achieve good fit Cross validation regularization and ensemble methods are some of the popular methods Another way to balance bias and variance is through the use of different model architectures and hyperparameter tuning For example using a more complex model with more features and parameters can decrease bias but increase variance while using a simpler model with fewer features and parameters can decrease variance but increase bias It s important to remember that bias and variance are not always independent and in some cases reducing one may also reduce the other For example increasing the amount of training data can reduce both bias and variance First let s start with the mathematical equations In a linear regression model the equation for predicting a continuous target variable y based on a single input variable x is y mx b εwhere m is the slope of the line b is the y intercept and εis the error term Bias refers to the difference between the predicted values of the model and the true values of the data We can express this mathematically as Bias E mx b y where E denotes the expected value Variance on the other hand refers to the variability of a model s predictions for different training sets We can express this mathematically as Variance E mx b E mx b Now let s take a look at a machine learning example Imagine we are trying to predict the price of a house based on its square footage We train a linear regression model using a dataset of houses The model has a low bias and high variance meaning it fits the training data well but it doesn t generalize well to new data When we test the model on a new dataset of houses we find that its predictions are far off from the true prices In this example our model is overfitting the training data To improve its performance we can try to reduce the variance by using a simpler model such as a linear regression with regularization or by increasing the amount of training data In summary bias and variance are two key concepts in machine learning and data science that describe the errors that can occur in model predictions Bias refers to the difference between the predicted values of a model and the true values of the data and variance refers to the variability of a model s predictions for different training sets Balancing these errors is crucial for achieving good performance and finding a model that generalizes well to new data	2023-01-26 05:47:04
医療系	医療介護 CBnews	電子処方箋に対応可能154施設、厚労省修正-26日に運用スタート	https://www.cbnews.jp/news/entry/20230126142331	医療機関	2023-01-26 14:35:00
ニュース	BBC News - Home	Ukraine war: Zelensky urges speedy delivery of Western tanks	https://www.bbc.co.uk/news/world-64408504?at_medium=RSS&at_campaign=KARANGA	numbers	2023-01-26 05:45:48
ニュース	BBC News - Home	The Papers: 'Raab bullying probe widens' and 'hold back Putin'	https://www.bbc.co.uk/news/blogs-the-papers-64408095?at_medium=RSS&at_campaign=KARANGA	ukraine	2023-01-26 05:25:00
IT	週刊アスキー	ebay、日本から「スマホアプリ」で出品に対応	https://weekly.ascii.jp/elem/000/004/122/4122144/	月日	2023-01-26 14:45:00