投稿時間:2023-02-04 19:13:23 RSSフィード2023-02-04 19:00 分まとめ(15件)

カテゴリー等	サイト名等	記事タイトル・トレンドワード等	リンクURL	頻出ワード・要約等/検索ボリューム	登録日
TECH	Techable（テッカブル）	6割の事業者がチャージバックの経験あり、顧客の友人によるカード番号悪用も3割近くが経験	https://techable.jp/archives/195088	株式会社	2023-02-04 09:00:26
python	Pythonタグが付けられた新着投稿 - Qiita	Pythonのself	https://qiita.com/amenbo_dayo/items/30eb24bb775353ffa892	classclassna	2023-02-04 18:41:27
js	JavaScriptタグが付けられた新着投稿 - Qiita	<JavaScript> Memo: A Function to Order Amateur Radio Callsigns According to JARL Bureau's Rule	https://qiita.com/jr2khb/items/38e3c866d9f233e4b954	lt JavaScript gt Memo A Function to Order Amateur Radio Callsigns According to JARL Bureau x s RuleGoalTo issue QSL cards contact confirmation cards to amateur radio stations we need to sort the…	2023-02-04 18:39:28
js	JavaScriptタグが付けられた新着投稿 - Qiita	JavaScriptでデザインパターン①〜シングルトン	https://qiita.com/wooooo/items/4cb72979f9e23aef12c8	javascript	2023-02-04 18:11:49
AWS	AWSタグが付けられた新着投稿 - Qiita	【AWS S3】直接アップロードしたファイルが公開できない	https://qiita.com/Oidon/items/ead63a10774a311d6fdc	一般男性	2023-02-04 18:37:54
Docker	dockerタグが付けられた新着投稿 - Qiita	Docker＋nginx＋Laravel シンボリックリンク	https://qiita.com/asaikazuto0407/items/6b7998ba979469ac73ef	phpartisan	2023-02-04 18:28:45
Linux	CentOSタグが付けられた新着投稿 - Qiita	Web三層構造への理解を深める。仮想マシンを3台構築し、pingコマンドで各サーバー同士の疎通確認を行う。	https://qiita.com/ts9130/items/bf48cd62d256a9cba649	linux	2023-02-04 18:16:26
GCP	gcpタグが付けられた新着投稿 - Qiita	Google Cloudアップデート (1/26-2/1/2022)	https://qiita.com/kenzkenz/items/34cac80446019267e0f5	tensorflowscikitlearn	2023-02-04 18:30:34
海外TECH	DEV Community	Decision Trees: Advantages, Disadvantages, and Applications	https://dev.to/anurag629/decision-trees-advantages-disadvantages-and-applications-25b2	Decision Trees Advantages Disadvantages and Applications Introduction to Decision TreesDecision trees are a type of supervised machine learning algorithm used for both regression and classification problems They are tree based models that split the data into smaller subsets based on certain conditions The final output is obtained by combining the results of multiple splits Decision trees are simple interpretable and easy to visualize making them a great choice for data scientists Here s a simple example of how a decision tree could be used for a binary classification problem Let s say we have a dataset of students and their study habits and we want to classify each student as either pass or fail The features in our dataset might include the number of hours the student studies per week their test scores and their attendance record Here s an example of how a decision tree could look for this problem In this example we start at the root node and ask the first question Does the student study gt hours per week Depending on the answer we follow the corresponding branch and ask the next question For example if the answer is YES we then ask Does the student have a test score gt and so on until we reach a leaf node which represents the final prediction In this way the decision tree learns from the training data to make predictions for new unseen data The idea is to create a tree where each internal node represents a feature that maximizes the separation between the classes in the data and each leaf node represents a class label Advantages of Decision TreesEasy to understand Decision trees are easy to understand and interpret even for non technical people This makes them a great tool for explaining complex models to stakeholders Handle Non Linear Relationships Decision trees can handle non linear relationships between features and target variables making them a great choice for datasets with complex relationships Handle Missing Values Decision trees can handle missing values in the data making them a great choice for datasets with missing values Little Data Preparation Decision trees require little data preparation making them a great choice for datasets that have not been cleaned or preprocessed Disadvantages of Decision TreesOverfitting Decision trees are prone to overfitting especially when the tree is deep and complex This can result in poor generalization performance on unseen data Instability Decision trees can be unstable meaning that small changes in the data can result in different trees This makes them less suitable for datasets with high variability Where to Use Decision TreesClassification Problems Decision trees are a great choice for classification problems especially when the relationships between features and target variables are non linear Regression Problems Decision trees can also be used for regression problems although they are not as commonly used as they are for classification problems Where Not to Use Decision TreesHigh Dimensional Data Decision trees are not well suited for high dimensional data as the number of splits required to split the data becomes very large Large Datasets Decision trees can become slow and inefficient on large datasets making them a poor choice for large datasets Example Predicting DiabetesTo illustrate the use of decision trees let s consider a simple example of predicting diabetes based on certain features Here is an example of how to build a decision tree for this problem in Python using the scikit learn library import pandas as pdfrom sklearn tree import DecisionTreeClassifierfrom sklearn model selection import train test split load the datadata pd read csv diabetes data csv split the data into training and testing setstrain data test data train target test target train test split data drop Outcome axis data Outcome test size build the decision tree modelmodel DecisionTreeClassifier model fit train data train target evaluate the model on the testing setaccuracy model score test data test target print Accuracy accuracy In this example we used the diabetes data csv dataset which contains various features related to diabetes such as age blood pressure and glucose level The target variable Outcome indicates whether the patient has diabetes or not We split the data into training and testing sets and then built a decision tree model using the DecisionTreeClassifier class from scikit learn Finally we evaluated the model on the testing set and printed the accuracy Visualizing Decision TreesOne of the benefits of decision trees is their interpretability and easy visualization In Python we can visualize decision trees using the plot tree function from the sklearn library Here is an example of how to visualize the decision tree from the previous example from sklearn import treeimport matplotlib pyplot as pltplt figure figsize tree plot tree model filled True plt show In this example we import the tree module from the sklearn library and the matplotlib pyplot module for plotting Then we use the plot tree function to visualize the decision tree and display it using the show function from matplotlib pyplot ConclusionIn conclusion decision trees are a powerful and simple machine learning algorithm that can be used for both regression and classification problems They are easy to understand and visualize and can handle non linear relationships and missing values in the data However they can also be prone to overfitting and instability and are not well suited for high dimensional data or large datasets With the right data and appropriate modifications decision trees can be a great tool for data scientists	2023-02-04 09:38:03
ニュース	@日本経済新聞 電子版	渋谷が「泊まれる街」に　東急百貨店の本店閉店にみる主役交代 https://t.co/8A1b6N3VmO https://t.co/sNXLoz4PoM	https://twitter.com/nikkei/statuses/1621803268446851074	東急百貨店	2023-02-04 09:30:05
海外ニュース	Japan Times latest articles	Chinese balloon threatens to deflate momentum toward improving Sino-U.S. ties	https://www.japantimes.co.jp/news/2023/02/04/asia-pacific/politics-diplomacy-asia-pacific/us-china-relationship-spy-balloon/	Chinese balloon threatens to deflate momentum toward improving Sino U S tiesWhile some analysts said the U S decision to cancel high level talks illustrated major hurdles in Sino American ties others said a chance for another meeting could	2023-02-04 18:33:49
海外ニュース	Japan Times latest articles	Fears of Russian nuclear weapons use have diminished, but could re-emerge	https://www.japantimes.co.jp/news/2023/02/04/world/russia-us-nuclear-red-lines/	Fears of Russian nuclear weapons use have diminished but could re emergeNearly a year into the war in Ukraine U S policymakers and intelligence analysts have more confidence that they understand at least some of President Vladimir	2023-02-04 18:11:23
海外ニュース	Japan Times latest articles	Japanese firms urged to address human rights issues in supply chains	https://www.japantimes.co.jp/news/2023/02/04/business/japan-firms-supply-chains-human-rights/	Japanese firms urged to address human rights issues in supply chains If companies do not tackle this issue in the right way they could be shut out from the European and U S markets Asako Okai director	2023-02-04 18:03:13
ニュース	BBC News - Home	Elon Musk found not guilty of fraud over Tesla tweet	https://www.bbc.co.uk/news/world-us-canada-64520157?at_medium=RSS&at_campaign=KARANGA	charges	2023-02-04 09:37:31
ニュース	BBC News - Home	Covid: Last chance for adults under-50 to get booster	https://www.bbc.co.uk/news/health-64496025?at_medium=RSS&at_campaign=KARANGA	booster	2023-02-04 09:36:47