How to Upgrade Judges with Machine Learning どの被告人を保釈すべきか 機械学習で裁判官に助言 どの被告人を保釈すべきか、機械学習の試験運用で裁判官の判断を高精度に補助できることがわかった。有色人種への偏見が減り、拘留の必要のない被告人を保釈できれば、米国で多額の税金が使われている収監費用も削減できる。 by Tom Simonite2017.03.07 171 75 5 2 裁判を待つ刑事被告人のうち、誰を保釈し、誰を拘置所に拘留しておくべきなのか？ ソフトウェアによって裁判官の判断を補助し、保釈中の再犯件数や刑事裁判中に収監される被告人の数を削減すれば、米国で多額の税金が使われている収監費用を削減できる可能性がある。 全米経済研究所（NBER）は、経済学者とコンピューター科学者による新たな研究で、ニューヨーク市で発生した事件のデータ数万件により
This page is a collection of select recorded lectures on AI given by Lex Fridman and others. Deep Learning (2020) Notice: Undefined index: title in /var/www/lex_fridman/wordpress/wp-content/themes/twentytwelve-child/grid-vid.php on line 68 Notice: Undefined variable: extra1 in /var/www/lex_fridman/wordpress/wp-content/themes/twentytwelve-child/grid-vid.php on line 98 ">
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. For a brief introduction to the ideas behind the library, you can read the introductory notes or the paper. Visit the installation page to see how you can download the package and get started with it. You can browse the example gallery
seaborn.pairplot# seaborn.pairplot(data, *, hue=None, hue_order=None, palette=None, vars=None, x_vars=None, y_vars=None, kind='scatter', diag_kind='auto', markers=None, height=2.5, aspect=1, corner=False, dropna=False, plot_kws=None, diag_kws=None, grid_kws=None, size=None)# Plot pairwise relationships in a dataset. By default, this function will create a grid of Axes such that each numeric variab
seaborn.PairGrid# class seaborn.PairGrid(data, *, hue=None, vars=None, x_vars=None, y_vars=None, hue_order=None, palette=None, hue_kws=None, corner=False, diag_sharey=True, height=2.5, aspect=1, layout_pad=0.5, despine=True, dropna=False)# Subplot grid for plotting pairwise relationships in a dataset. This object maps each variable in a dataset onto a column and row in a grid of multiple axes. Dif
seaborn.kdeplot# seaborn.kdeplot(data=None, *, x=None, y=None, hue=None, weights=None, palette=None, hue_order=None, hue_norm=None, color=None, fill=None, multiple='layer', common_norm=True, common_grid=False, cumulative=False, bw_method='scott', bw_adjust=1, warn_singular=True, log_scale=None, levels=10, thresh=0.05, gridsize=200, cut=3, clip=None, legend=True, cbar=False, cbar_ax=None, cbar_kws=
Ctrl+K Site Navigation Installing Gallery Tutorial API Releases Citing FAQ GitHub StackOverflow Twitter On this page User guide and tutorial# An introduction to seaborn A high-level API for statistical graphics Multivariate views on complex datasets Opinionated defaults and flexible customization API Overview# Overview of seaborn plotting functions Similar functions for similar tasks Figure-level
Controlling figure aesthetics# Drawing attractive figures is important. When making figures for yourself, as you explore a dataset, it’s nice to have plots that are pleasant to look at. Visualizations are also central to communicating quantitative insights to an audience, and in that setting it’s even more necessary to have figures that catch the attention and draw a viewer in. Matplotlib is highl
Ctrl+K Site Navigation Installing Gallery Tutorial API Releases Citing FAQ GitHub StackOverflow Twitter Example gallery# lmplot scatterplot lineplot displot relplot catplot boxplot violinplot relplot jointplot histplot boxplot stripplot JointGrid jointplot FacetGrid boxenplot scatterplot lmplot FacetGrid heatmap JointGrid kdeplot displot displot lmplot PairGrid PairGrid PairGrid barplot kdeplot ba
seaborn.heatmap# seaborn.heatmap(data, *, vmin=None, vmax=None, cmap=None, center=None, robust=False, annot=None, fmt='.2g', annot_kws=None, linewidths=0, linecolor='white', cbar=True, cbar_kws=None, cbar_ax=None, square=False, xticklabels='auto', yticklabels='auto', mask=None, ax=None, **kwargs)# Plot rectangular data as a color-encoded matrix. This is an Axes-level function and will draw the hea
Building structured multi-plot grids# When exploring multi-dimensional data, a useful approach is to draw multiple instances of the same plot on different subsets of your dataset. This technique is sometimes called either “lattice” or “trellis” plotting, and it is related to the idea of “small multiples”. It allows a viewer to quickly extract a large amount of information about a complex dataset.
Visualizing distributions of data# An early step in any effort to analyze or model data should be to understand how the variables are distributed. Techniques for distribution visualization can provide quick answers to many important questions. What range do the observations cover? What is their central tendency? Are they heavily skewed in one direction? Is there evidence for bimodality? Are there
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. For a brief introduction to the ideas behind the library, you can read the introductory notes or the paper. Visit the installation page to see how you can download the package and get started with it. You can browse the example gallery
Nathaniel Read Silver (born January 13, 1978) is an American statistician, political analyst, author, sports gambler, and poker player who analyzes baseball, basketball and elections. He is the founder of FiveThirtyEight and held the position of editor-in-chief there, along with being a special correspondent for ABC News until May 2023.[2] Since departing FiveThirtyEight, Silver has been publishin
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FRED Tools and resources to find and use economic data worldwide.
戦略国際問題研究所（せんりゃくこくさいもんだいけんきゅうじょ、英語: Center for Strategic and International Studies, CSIS）は、アメリカ合衆国のワシントンD.C.に本部を置くシンクタンクである。 1962年にジョージタウン大学が設けた戦略国際問題研究所（CSIS）が、後に学外組織として発展したものである[1]。現在のフルタイム常勤職員は220人[2]。議長はトーマス・プリッツカー（Thomas J. Pritzker）、所長兼CEOはジョン・ヘイムリ（John J. Hamre）[2]。 全世界のシンクタンクをランク付けしたペンシルベニア大学によるレポート（Go to think tank indexの2014年版）によれば、CSISは防衛、国家安全保障(Table 14) で世界第1位、外交政策、国際関係論(Table 31) で第5位
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