{"id":400015,"date":"2024-06-29T15:00:20","date_gmt":"2024-06-29T15:00:20","guid":{"rendered":"http:\/\/savepearlharbor.com\/?p=400015"},"modified":"-0001-11-30T00:00:00","modified_gmt":"2024-06-29T15:00:20","slug":"","status":"publish","type":"post","link":"https:\/\/savepearlharbor.com\/?p=400015","title":{"rendered":"<span>\u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u043c \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b \u043f\u043b\u0435\u0439-\u043e\u0444\u0444 \u0438 \u043f\u043e\u0431\u0435\u0434\u0438\u0442\u0435\u043b\u044f \u0415\u0432\u0440\u043e 2024 \u043f\u0440\u0438 \u043f\u043e\u043c\u043e\u0449\u0438 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0438 \u0447\u0430\u0442\u0430 GPT 4.0 (\u0447\u0430\u0441\u0442\u044c 2)<\/span>"},"content":{"rendered":"<div><!--[--><!--]--><\/div>\n<div id=\"post-content-body\">\n<div>\n<div class=\"article-formatted-body article-formatted-body article-formatted-body_version-2\">\n<div xmlns=\"http:\/\/www.w3.org\/1999\/xhtml\">\n<p><strong>\u0414\u0438\u0441\u043a\u043b\u0435\u0439\u043c\u0435\u0440<\/strong><\/p>\n<p>\u041f\u043e \u043f\u0440\u0438\u0432\u0435\u0434\u0435\u043d\u043d\u044b\u043c \u043d\u0438\u0436\u0435 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0430\u043c \u043d\u0435 \u0441\u0442\u043e\u0438\u0442 \u0434\u0435\u043b\u0430\u0442\u044c \u0441\u0442\u0430\u0432\u043a\u0438, \u0442.\u043a. \u043e\u043d\u0438 \u043d\u0435 \u0443\u0447\u0438\u0442\u044b\u0432\u0430\u044e\u0442 \u0431\u0443\u043a\u043c\u0435\u043a\u0435\u0440\u0441\u043a\u0443\u044e \u043c\u0430\u0440\u0436\u0443, \u0444\u043e\u0440\u043c\u0443 \u043a\u043e\u043c\u0430\u043d\u0434, \u0430 \u0442\u0430\u043a\u0436\u0435 \u043c\u043d\u043e\u0433\u043e \u0434\u0440\u0443\u0433\u0438\u0445 \u0444\u0430\u043a\u0442\u043e\u0440\u043e\u0432. \u0412 \u0446\u0435\u043b\u043e\u043c \u0441\u0442\u0430\u0432\u043a\u0438 \u043d\u0430 \u0441\u043f\u043e\u0440\u0442 \u0432\u0435\u0441\u044c\u043c\u0430 \u0441\u043f\u0435\u0446\u0438\u0444\u0438\u0447\u043d\u043e\u0435 \u0437\u0430\u043d\u044f\u0442\u0438\u0435, \u0440\u0430\u0441\u0441\u0447\u0438\u0442\u0430\u043d\u043d\u043e\u0435 \u0432\u043e \u043c\u043d\u043e\u0433\u043e\u043c \u043d\u0430 \u043f\u0441\u0438\u0445\u043e\u043b\u043e\u0433\u0438\u044e \u0447\u0435\u043b\u043e\u0432\u0435\u043a\u0430, \u0441\u043a\u0440\u044b\u0442\u044b\u0435 \u0441\u043b\u0430\u0431\u043e\u0441\u0442\u0438 \u0438 \u0442. \u0434., \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u0432 \u0446\u0435\u043b\u043e\u043c \u043d\u0435 \u0441\u0442\u043e\u0438\u0442 \u0441\u0442\u0430\u0432\u0438\u0442\u044c \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0430\u043d\u0438\u0438 \u043b\u044e\u0431\u044b\u0445 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u043e\u0432 \u0432 \u0438\u043d\u0442\u0435\u0440\u043d\u0435\u0442\u0435.<\/p>\n<p>\u0420\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0439 \u0433\u0440\u0443\u043f\u043f\u043e\u0432\u043e\u0433\u043e \u044d\u0442\u0430\u043f\u0430:<\/p>\n<ul>\n<li>\n<p><strong>Correct Predictions (+):<\/strong> 15<\/p>\n<\/li>\n<li>\n<p><strong>Incorrect Predictions (-):<\/strong> 21<\/p>\n<\/li>\n<li>\n<p><strong>Percentage of Correct Predictions:<\/strong> 41.67%<\/p>\n<\/li>\n<li>\n<p><strong>Sum of Coefficients for Correct Predictions:<\/strong> 32.2<\/p>\n<\/li>\n<\/ul>\n<p>\u041f\u0440\u043e\u0446\u0435\u043d\u0442 \u0443\u0434\u0430\u0447\u043d\u043e\u0433\u043e \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f \u043e\u043a\u0430\u0437\u0430\u043b\u0441\u044f \u043d\u0430 16 \u043f\u0440\u043e\u0446\u0435\u043d\u0442\u043e\u0432 \u043d\u0438\u0436\u0435, \u0447\u0435\u043c \u0431\u044b\u043b\u043e \u0437\u0430\u044f\u0432\u043b\u0435\u043d\u043e \u0438\u0437\u043d\u0430\u0447\u0430\u043b\u044c\u043d\u043e. \u041f\u0440\u0438 \u044d\u0442\u043e\u043c \u043f\u0435\u0440\u0432\u044b\u0435 \u0434\u0432\u0430 \u0442\u0443\u0440\u0430 \u0434\u0435\u0440\u0436\u0430\u043b\u0441\u044f \u0434\u043e\u0432\u043e\u043b\u044c\u043d\u043e \u0432\u044b\u0441\u043e\u043a\u0438\u0439 \u043f\u0440\u043e\u0446\u0435\u043d\u0442 \u043f\u0440\u043e\u0445\u043e\u0434\u0430, \u043d\u043e \u0432 \u0442\u0440\u0435\u0442\u044c\u0435\u043c \u0442\u0443\u0440\u0435, \u0433\u0434\u0435 \u0434\u043b\u044f \u043c\u043d\u043e\u0433\u0438\u0445 \u043a\u043e\u043c\u0430\u043d\u0434 \u0432\u043e\u043f\u0440\u043e\u0441 \u0432\u044b\u0445\u043e\u0434\u0430 \u0438\u0437 \u0433\u0440\u0443\u043f\u043f\u044b \u043d\u0435 \u0440\u0435\u0448\u0430\u043b\u0441\u044f, \u043f\u0440\u043e\u0446\u0435\u043d\u0442 \u0443\u0434\u0430\u0447\u043d\u044b\u0445 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u043e\u0432 \u0441\u0438\u043b\u044c\u043d\u043e \u0441\u043d\u0438\u0437\u0438\u043b\u0441\u044f.<\/p>\n<p>\u0414\u0435\u043b\u0430\u044f \u0441\u0442\u0430\u0432\u043a\u0438 \u043f\u043e 100 \u0434\u043e\u043b\u043b\u0430\u0440\u043e\u0432 \u043d\u0430 36 \u043c\u0430\u0442\u0447\u0435\u0439, \u043c\u044b \u0431\u044b \u043f\u043e\u0442\u0435\u0440\u044f\u043b\u0438 \u043f\u0440\u0438\u043c\u0435\u0440\u043d\u043e 380 \u0434\u043e\u043b\u043b\u0430\u0440\u043e\u0432. <\/p>\n<p>\u041f\u0440\u043e\u0433\u043d\u043e\u0437 \u043d\u0430 \u043f\u043b\u0435\u0439-\u043e\u0444\u0444:<\/p>\n<pre><code>library(randomForest) library(dplyr)  # \u0417\u0430\u0433\u0440\u0443\u0437\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 data &lt;- read.csv(\"filtered_results.csv\")  # \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u0430 date \u0432 \u0444\u043e\u0440\u043c\u0430\u0442 \u0434\u0430\u0442\u044b data$date &lt;- as.Date(data$date, format=\"%Y-%m-%d\")  # \u0421\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u0446\u0435\u043b\u0435\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 (\u0438\u0441\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435 \u043d\u0438\u0447\u044c\u0438\u0445) data$result &lt;- ifelse(data$home_score > data$away_score, 1, 0)  # \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 \u0435\u0434\u0438\u043d\u044b\u0439 \u0444\u043e\u0440\u043c\u0430\u0442 home_games &lt;- data %>%     select(team = home_team, opponent = away_team, score = home_score, opponent_score = away_score, result)  away_games &lt;- data %>%     select(team = away_team, opponent = home_team, score = away_score, opponent_score = home_score, result) %>%     mutate(result = ifelse(result == 1, 0, 1))  all_games &lt;- bind_rows(home_games, away_games)  # \u0421\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u043d\u043e\u0432\u044b\u0445 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432 team_stats &lt;- all_games %>%     group_by(team) %>%     summarise(total_games = n(),               total_win_rate = mean(result == 1),               total_avg_score = mean(score))  # \u041f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0434\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0438 data &lt;- data %>%     left_join(team_stats, by = c(\"home_team\" = \"team\")) %>%     rename(home_team_total_games = total_games,            home_team_total_win_rate = total_win_rate,            home_team_total_avg_score = total_avg_score) %>%     left_join(team_stats, by = c(\"away_team\" = \"team\")) %>%     rename(away_team_total_games = total_games,            away_team_total_win_rate = total_win_rate,            away_team_total_avg_score = total_avg_score)  # \u041f\u0440\u043e\u0432\u0435\u0440\u043a\u0430 \u0438 \u0437\u0430\u043c\u0435\u043d\u0430 NA \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 data[is.na(data)] &lt;- 0  # \u041f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0434\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0438 features &lt;- c(\"home_team_total_win_rate\", \"away_team_total_win_rate\",                \"home_team_total_games\", \"away_team_total_games\",                \"home_team_total_avg_score\", \"away_team_total_avg_score\") X &lt;- data[features] y &lt;- factor(data$result)  # \u0420\u0430\u0437\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u043d\u0430 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0443\u044e \u0438 \u0442\u0435\u0441\u0442\u043e\u0432\u0443\u044e \u0432\u044b\u0431\u043e\u0440\u043a\u0438 set.seed(42) train_indices &lt;- sample(seq_len(nrow(data)), size = 0.8 * nrow(data)) X_train &lt;- X[train_indices, ] y_train &lt;- y[train_indices] X_test &lt;- X[-train_indices, ] y_test &lt;- y[-train_indices]  # \u041e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 Random Forest rf_model &lt;- randomForest(X_train, y_train, ntree=200, mtry=3, importance=TRUE)  # \u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0435 \u043d\u0430 \u0442\u0435\u0441\u0442\u043e\u0432\u043e\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0435 y_pred &lt;- predict(rf_model, X_test) accuracy &lt;- sum(y_pred == y_test) \/ length(y_test) print(paste(\"Accuracy:\", accuracy))  # \u0424\u0443\u043d\u043a\u0446\u0438\u044f \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 \u043c\u0430\u0442\u0447\u0435\u0439 predict_matches &lt;- function(matches, model, stats) {   matches &lt;- matches %>%     left_join(stats, by = c(\"home_team\" = \"team\")) %>%     rename(home_team_total_win_rate = total_win_rate,            home_team_total_games = total_games,            home_team_total_avg_score = total_avg_score) %>%     left_join(stats, by = c(\"away_team\" = \"team\")) %>%     rename(away_team_total_win_rate = total_win_rate,            away_team_total_games = total_games,            away_team_total_avg_score = total_avg_score)    matches[is.na(matches)] &lt;- 0    predictions &lt;- predict(model, matches[features])   results &lt;- ifelse(predictions == 1, \"Home Win\", \"Away Win\")      return(results) }  # 1\/8 \u0444\u0438\u043d\u0430\u043b\u0430 round_of_16 &lt;- data.frame(   home_team = c(\"Switzerland\", \"Germany\", \"England\", \"Spain\", \"France\", \"Portugal\", \"Romania\", \"Austria\"),   away_team = c(\"Italy\", \"Denmark\", \"Slovakia\", \"Georgia\", \"Belgium\", \"Slovenia\", \"Netherlands\", \"Turkey\") )  round_of_16_results &lt;- predict_matches(round_of_16, rf_model, team_stats) round_of_16  # 1\/4 \u0444\u0438\u043d\u0430\u043b\u0430 quarterfinals &lt;- data.frame(   home_team = c(     ifelse(round_of_16_results[1] == \"Home Win\", \"Switzerland\", \"Italy\"),     ifelse(round_of_16_results[2] == \"Home Win\", \"Germany\", \"Denmark\"),     ifelse(round_of_16_results[3] == \"Home Win\", \"England\", \"Slovakia\"),     ifelse(round_of_16_results[4] == \"Home Win\", \"Spain\", \"Georgia\")   ),   away_team = c(     ifelse(round_of_16_results[5] == \"Home Win\", \"France\", \"Belgium\"),     ifelse(round_of_16_results[6] == \"Home Win\", \"Portugal\", \"Slovenia\"),     ifelse(round_of_16_results[7] == \"Home Win\", \"Romania\", \"Netherlands\"),     ifelse(round_of_16_results[8] == \"Home Win\", \"Austria\", \"Turkey\")   ) )  quarterfinal_results &lt;- predict_matches(quarterfinals, rf_model, team_stats) quarterfinals  # 1\/2 \u0444\u0438\u043d\u0430\u043b\u0430 semifinals &lt;- data.frame(   home_team = c(     ifelse(quarterfinal_results[1] == \"Home Win\", quarterfinals$home_team[1], quarterfinals$away_team[1]),     ifelse(quarterfinal_results[2] == \"Home Win\", quarterfinals$home_team[2], quarterfinals$away_team[2])   ),   away_team = c(     ifelse(quarterfinal_results[3] == \"Home Win\", quarterfinals$home_team[3], quarterfinals$away_team[3]),     ifelse(quarterfinal_results[4] == \"Home Win\", quarterfinals$home_team[4], quarterfinals$away_team[4])   ) )  semifinal_results &lt;- predict_matches(semifinals, rf_model, team_stats) semifinals  # \u0424\u0438\u043d\u0430\u043b final &lt;- data.frame(   home_team = ifelse(semifinal_results[1] == \"Home Win\", semifinals$home_team[1], semifinals$away_team[1]),   away_team = ifelse(semifinal_results[2] == \"Home Win\", semifinals$home_team[2], semifinals$away_team[2]) )  final_result &lt;- predict_matches(final, rf_model, team_stats) final  # \u0412\u044b\u0432\u043e\u0434 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 print(\"1\/8 \u0444\u0438\u043d\u0430\u043b\u0430 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b:\") for (i in 1:nrow(round_of_16)) {   print(paste(round_of_16$home_team[i], \"vs\", round_of_16$away_team[i], \"-> Prediction:\", round_of_16_results[i])) }  print(\"1\/4 \u0444\u0438\u043d\u0430\u043b\u0430 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b:\") for (i in 1:nrow(quarterfinals)) {   print(paste(quarterfinals$home_team[i], \"vs\", quarterfinals$away_team[i], \"-> Prediction:\", quarterfinal_results[i])) }  print(\"1\/2 \u0444\u0438\u043d\u0430\u043b\u0430 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b:\") for (i in 1:nrow(semifinals)) {   print(paste(semifinals$home_team[i], \"vs\", semifinals$away_team[i], \"-> Prediction:\", semifinal_results[i])) }  print(\"\u0424\u0438\u043d\u0430\u043b \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442:\") print(paste(final$home_team, \"vs\", final$away_team, \"-> Prediction:\", final_result)) <\/code><\/pre>\n<p>1\/8 \u0444\u0438\u043d\u0430\u043b\u0430:<\/p>\n<p>[1] &#171;Switzerland vs Italy -> Prediction: Away Win&#187;<\/p>\n<p>[1] &#171;Germany vs Denmark -> Prediction: Away Win&#187;<\/p>\n<p>[1] &#171;England vs Slovakia -> Prediction: Home Win&#187;<\/p>\n<p>[1] &#171;Spain vs Georgia -> Prediction: Home Win&#187;<\/p>\n<p>[1] &#171;France vs Belgium -> Prediction: Home Win&#187;<\/p>\n<p>[1] &#171;Portugal vs Slovenia -> Prediction: Home Win&#187;<\/p>\n<p>[1] &#171;Romania vs Netherlands -> Prediction: Away Win&#187;<\/p>\n<p>[1] &#171;Austria vs Turkey -> Prediction: Away Win&#187;<\/p>\n<p>\u041f\u0440\u043e\u0446\u0435\u043d\u0442 \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u0438 \u0432\u044b\u0440\u043e\u0441 &#8212; &#171;Accuracy: 0.724324324324324&#187;. \u041b\u043e\u0433\u0438\u0447\u043d\u043e, \u0443\u0447\u0438\u0442\u044b\u0432\u0430\u044f \u0438\u0433\u0440\u0443 \u043d\u0430 \u0434\u0432\u0430 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0430. <\/p>\n<p>\u0414\u0430\u043b\u0435\u0435 \u0431\u0443\u0434\u0443 \u0434\u0430\u0432\u0430\u0442\u044c \u043f\u0440\u043e\u0433\u043d\u043e\u0437 \u0432 \u043a\u043e\u043c\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u044f\u0445 \u0432 \u0437\u0430\u0432\u0438\u0441\u0438\u043c\u043e\u0441\u0442\u0438 \u043e\u0442 \u0440\u0435\u0430\u043b\u044c\u043d\u043e \u0441\u043b\u043e\u0436\u0438\u0432\u0448\u0438\u0445\u0441\u044f \u043f\u0430\u0440.<\/p>\n<p>\u0421\u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0439 \u0444\u0438\u043d\u0430\u043b: &#171;England vs Spain -> Prediction: Away Win&#187;<\/p>\n<p>\u041f\u043e\u0431\u0435\u0434\u0438\u0442\u0435\u043b\u0435\u043c \u0441\u043d\u043e\u0432\u0430 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0430 \u0418\u0441\u043f\u0430\u043d\u0438\u044f, \u043a\u043e\u044d\u0444\u0444\u0438\u0446\u0438\u0435\u043d\u0442 \u043d\u0430 \u043f\u043e\u0431\u0435\u0434\u0443 \u0441\u043d\u0438\u0437\u0438\u043b\u0441\u044f \u0441 9.00 \u0434\u043e 5.50.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<p><!----><!----><\/div>\n<p><!----><!----><br \/> \u0441\u0441\u044b\u043b\u043a\u0430 \u043d\u0430 \u043e\u0440\u0438\u0433\u0438\u043d\u0430\u043b \u0441\u0442\u0430\u0442\u044c\u0438 <a href=\"https:\/\/habr.com\/ru\/articles\/825510\/\"> https:\/\/habr.com\/ru\/articles\/825510\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<div><!--[--><!--]--><\/div>\n<div id=\"post-content-body\">\n<div>\n<div class=\"article-formatted-body article-formatted-body article-formatted-body_version-2\">\n<div xmlns=\"http:\/\/www.w3.org\/1999\/xhtml\">\n<p><strong>\u0414\u0438\u0441\u043a\u043b\u0435\u0439\u043c\u0435\u0440<\/strong><\/p>\n<p>\u041f\u043e \u043f\u0440\u0438\u0432\u0435\u0434\u0435\u043d\u043d\u044b\u043c \u043d\u0438\u0436\u0435 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0430\u043c \u043d\u0435 \u0441\u0442\u043e\u0438\u0442 \u0434\u0435\u043b\u0430\u0442\u044c \u0441\u0442\u0430\u0432\u043a\u0438, \u0442.\u043a. \u043e\u043d\u0438 \u043d\u0435 \u0443\u0447\u0438\u0442\u044b\u0432\u0430\u044e\u0442 \u0431\u0443\u043a\u043c\u0435\u043a\u0435\u0440\u0441\u043a\u0443\u044e \u043c\u0430\u0440\u0436\u0443, \u0444\u043e\u0440\u043c\u0443 \u043a\u043e\u043c\u0430\u043d\u0434, \u0430 \u0442\u0430\u043a\u0436\u0435 \u043c\u043d\u043e\u0433\u043e \u0434\u0440\u0443\u0433\u0438\u0445 \u0444\u0430\u043a\u0442\u043e\u0440\u043e\u0432. \u0412 \u0446\u0435\u043b\u043e\u043c \u0441\u0442\u0430\u0432\u043a\u0438 \u043d\u0430 \u0441\u043f\u043e\u0440\u0442 \u0432\u0435\u0441\u044c\u043c\u0430 \u0441\u043f\u0435\u0446\u0438\u0444\u0438\u0447\u043d\u043e\u0435 \u0437\u0430\u043d\u044f\u0442\u0438\u0435, \u0440\u0430\u0441\u0441\u0447\u0438\u0442\u0430\u043d\u043d\u043e\u0435 \u0432\u043e \u043c\u043d\u043e\u0433\u043e\u043c \u043d\u0430 \u043f\u0441\u0438\u0445\u043e\u043b\u043e\u0433\u0438\u044e \u0447\u0435\u043b\u043e\u0432\u0435\u043a\u0430, \u0441\u043a\u0440\u044b\u0442\u044b\u0435 \u0441\u043b\u0430\u0431\u043e\u0441\u0442\u0438 \u0438 \u0442. \u0434., \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u0432 \u0446\u0435\u043b\u043e\u043c \u043d\u0435 \u0441\u0442\u043e\u0438\u0442 \u0441\u0442\u0430\u0432\u0438\u0442\u044c \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0430\u043d\u0438\u0438 \u043b\u044e\u0431\u044b\u0445 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u043e\u0432 \u0432 \u0438\u043d\u0442\u0435\u0440\u043d\u0435\u0442\u0435.<\/p>\n<p>\u0420\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0439 \u0433\u0440\u0443\u043f\u043f\u043e\u0432\u043e\u0433\u043e \u044d\u0442\u0430\u043f\u0430:<\/p>\n<ul>\n<li>\n<p><strong>Correct Predictions (+):<\/strong> 15<\/p>\n<\/li>\n<li>\n<p><strong>Incorrect Predictions (-):<\/strong> 21<\/p>\n<\/li>\n<li>\n<p><strong>Percentage of Correct Predictions:<\/strong> 41.67%<\/p>\n<\/li>\n<li>\n<p><strong>Sum of Coefficients for Correct Predictions:<\/strong> 32.2<\/p>\n<\/li>\n<\/ul>\n<p>\u041f\u0440\u043e\u0446\u0435\u043d\u0442 \u0443\u0434\u0430\u0447\u043d\u043e\u0433\u043e \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f \u043e\u043a\u0430\u0437\u0430\u043b\u0441\u044f \u043d\u0430 16 \u043f\u0440\u043e\u0446\u0435\u043d\u0442\u043e\u0432 \u043d\u0438\u0436\u0435, \u0447\u0435\u043c \u0431\u044b\u043b\u043e \u0437\u0430\u044f\u0432\u043b\u0435\u043d\u043e \u0438\u0437\u043d\u0430\u0447\u0430\u043b\u044c\u043d\u043e. \u041f\u0440\u0438 \u044d\u0442\u043e\u043c \u043f\u0435\u0440\u0432\u044b\u0435 \u0434\u0432\u0430 \u0442\u0443\u0440\u0430 \u0434\u0435\u0440\u0436\u0430\u043b\u0441\u044f \u0434\u043e\u0432\u043e\u043b\u044c\u043d\u043e \u0432\u044b\u0441\u043e\u043a\u0438\u0439 \u043f\u0440\u043e\u0446\u0435\u043d\u0442 \u043f\u0440\u043e\u0445\u043e\u0434\u0430, \u043d\u043e \u0432 \u0442\u0440\u0435\u0442\u044c\u0435\u043c \u0442\u0443\u0440\u0435, \u0433\u0434\u0435 \u0434\u043b\u044f \u043c\u043d\u043e\u0433\u0438\u0445 \u043a\u043e\u043c\u0430\u043d\u0434 \u0432\u043e\u043f\u0440\u043e\u0441 \u0432\u044b\u0445\u043e\u0434\u0430 \u0438\u0437 \u0433\u0440\u0443\u043f\u043f\u044b \u043d\u0435 \u0440\u0435\u0448\u0430\u043b\u0441\u044f, \u043f\u0440\u043e\u0446\u0435\u043d\u0442 \u0443\u0434\u0430\u0447\u043d\u044b\u0445 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u043e\u0432 \u0441\u0438\u043b\u044c\u043d\u043e \u0441\u043d\u0438\u0437\u0438\u043b\u0441\u044f.<\/p>\n<p>\u0414\u0435\u043b\u0430\u044f \u0441\u0442\u0430\u0432\u043a\u0438 \u043f\u043e 100 \u0434\u043e\u043b\u043b\u0430\u0440\u043e\u0432 \u043d\u0430 36 \u043c\u0430\u0442\u0447\u0435\u0439, \u043c\u044b \u0431\u044b \u043f\u043e\u0442\u0435\u0440\u044f\u043b\u0438 \u043f\u0440\u0438\u043c\u0435\u0440\u043d\u043e 380 \u0434\u043e\u043b\u043b\u0430\u0440\u043e\u0432. <\/p>\n<p>\u041f\u0440\u043e\u0433\u043d\u043e\u0437 \u043d\u0430 \u043f\u043b\u0435\u0439-\u043e\u0444\u0444:<\/p>\n<pre><code>library(randomForest) library(dplyr)  # \u0417\u0430\u0433\u0440\u0443\u0437\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 data &lt;- read.csv(\"filtered_results.csv\")  # \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0441\u0442\u043e\u043b\u0431\u0446\u0430 date \u0432 \u0444\u043e\u0440\u043c\u0430\u0442 \u0434\u0430\u0442\u044b data$date &lt;- as.Date(data$date, format=\"%Y-%m-%d\")  # \u0421\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u0446\u0435\u043b\u0435\u0432\u043e\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 (\u0438\u0441\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435 \u043d\u0438\u0447\u044c\u0438\u0445) data$result &lt;- ifelse(data$home_score > data$away_score, 1, 0)  # \u041f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u0432 \u0435\u0434\u0438\u043d\u044b\u0439 \u0444\u043e\u0440\u043c\u0430\u0442 home_games &lt;- data %>%     select(team = home_team, opponent = away_team, score = home_score, opponent_score = away_score, result)  away_games &lt;- data %>%     select(team = away_team, opponent = home_team, score = away_score, opponent_score = home_score, result) %>%     mutate(result = ifelse(result == 1, 0, 1))  all_games &lt;- bind_rows(home_games, away_games)  # \u0421\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u043d\u043e\u0432\u044b\u0445 \u043f\u0440\u0438\u0437\u043d\u0430\u043a\u043e\u0432 team_stats &lt;- all_games %>%     group_by(team) %>%     summarise(total_games = n(),               total_win_rate = mean(result == 1),               total_avg_score = mean(score))  # \u041f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0434\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0438 data &lt;- data %>%     left_join(team_stats, by = c(\"home_team\" = \"team\")) %>%     rename(home_team_total_games = total_games,            home_team_total_win_rate = total_win_rate,            home_team_total_avg_score = total_avg_score) %>%     left_join(team_stats, by = c(\"away_team\" = \"team\")) %>%     rename(away_team_total_games = total_games,            away_team_total_win_rate = total_win_rate,            away_team_total_avg_score = total_avg_score)  # \u041f\u0440\u043e\u0432\u0435\u0440\u043a\u0430 \u0438 \u0437\u0430\u043c\u0435\u043d\u0430 NA \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 data[is.na(data)] &lt;- 0  # \u041f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 \u0434\u0430\u043d\u043d\u044b\u0445 \u0434\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0438 features &lt;- c(\"home_team_total_win_rate\", \"away_team_total_win_rate\",                \"home_team_total_games\", \"away_team_total_games\",                \"home_team_total_avg_score\", \"away_team_total_avg_score\") X &lt;- data[features] y &lt;- factor(data$result)  # \u0420\u0430\u0437\u0434\u0435\u043b\u0435\u043d\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u043d\u0430 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0443\u044e \u0438 \u0442\u0435\u0441\u0442\u043e\u0432\u0443\u044e \u0432\u044b\u0431\u043e\u0440\u043a\u0438 set.seed(42) train_indices &lt;- sample(seq_len(nrow(data)), size = 0.8 * nrow(data)) X_train &lt;- X[train_indices, ] y_train &lt;- y[train_indices] X_test &lt;- X[-train_indices, ] y_test &lt;- y[-train_indices]  # \u041e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 Random Forest rf_model &lt;- randomForest(X_train, y_train, ntree=200, mtry=3, importance=TRUE)  # \u041f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u0435 \u043d\u0430 \u0442\u0435\u0441\u0442\u043e\u0432\u043e\u0439 \u0432\u044b\u0431\u043e\u0440\u043a\u0435 y_pred &lt;- predict(rf_model, X_test) accuracy &lt;- sum(y_pred == y_test) \/ length(y_test) print(paste(\"Accuracy:\", accuracy))  # \u0424\u0443\u043d\u043a\u0446\u0438\u044f \u0434\u043b\u044f \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 \u043c\u0430\u0442\u0447\u0435\u0439 predict_matches &lt;- function(matches, model, stats) {   matches &lt;- matches %>%     left_join(stats, by = c(\"home_team\" = \"team\")) %>%     rename(home_team_total_win_rate = total_win_rate,            home_team_total_games = total_games,            home_team_total_avg_score = total_avg_score) %>%     left_join(stats, by = c(\"away_team\" = \"team\")) %>%     rename(away_team_total_win_rate = total_win_rate,            away_team_total_games = total_games,            away_team_total_avg_score = total_avg_score)    matches[is.na(matches)] &lt;- 0    predictions &lt;- predict(model, matches[features])   results &lt;- ifelse(predictions == 1, \"Home Win\", \"Away Win\")      return(results) }  # 1\/8 \u0444\u0438\u043d\u0430\u043b\u0430 round_of_16 &lt;- data.frame(   home_team = c(\"Switzerland\", \"Germany\", \"England\", \"Spain\", \"France\", \"Portugal\", \"Romania\", \"Austria\"),   away_team = c(\"Italy\", \"Denmark\", \"Slovakia\", \"Georgia\", \"Belgium\", \"Slovenia\", \"Netherlands\", \"Turkey\") )  round_of_16_results &lt;- predict_matches(round_of_16, rf_model, team_stats) round_of_16  # 1\/4 \u0444\u0438\u043d\u0430\u043b\u0430 quarterfinals &lt;- data.frame(   home_team = c(     ifelse(round_of_16_results[1] == \"Home Win\", \"Switzerland\", \"Italy\"),     ifelse(round_of_16_results[2] == \"Home Win\", \"Germany\", \"Denmark\"),     ifelse(round_of_16_results[3] == \"Home Win\", \"England\", \"Slovakia\"),     ifelse(round_of_16_results[4] == \"Home Win\", \"Spain\", \"Georgia\")   ),   away_team = c(     ifelse(round_of_16_results[5] == \"Home Win\", \"France\", \"Belgium\"),     ifelse(round_of_16_results[6] == \"Home Win\", \"Portugal\", \"Slovenia\"),     ifelse(round_of_16_results[7] == \"Home Win\", \"Romania\", \"Netherlands\"),     ifelse(round_of_16_results[8] == \"Home Win\", \"Austria\", \"Turkey\")   ) )  quarterfinal_results &lt;- predict_matches(quarterfinals, rf_model, team_stats) quarterfinals  # 1\/2 \u0444\u0438\u043d\u0430\u043b\u0430 semifinals &lt;- data.frame(   home_team = c(     ifelse(quarterfinal_results[1] == \"Home Win\", quarterfinals$home_team[1], quarterfinals$away_team[1]),     ifelse(quarterfinal_results[2] == \"Home Win\", quarterfinals$home_team[2], quarterfinals$away_team[2])   ),   away_team = c(     ifelse(quarterfinal_results[3] == \"Home Win\", quarterfinals$home_team[3], quarterfinals$away_team[3]),     ifelse(quarterfinal_results[4] == \"Home Win\", quarterfinals$home_team[4], quarterfinals$away_team[4])   ) )  semifinal_results &lt;- predict_matches(semifinals, rf_model, team_stats) semifinals  # \u0424\u0438\u043d\u0430\u043b final &lt;- data.frame(   home_team = ifelse(semifinal_results[1] == \"Home Win\", semifinals$home_team[1], semifinals$away_team[1]),   away_team = ifelse(semifinal_results[2] == \"Home Win\", semifinals$home_team[2], semifinals$away_team[2]) )  final_result &lt;- predict_matches(final, rf_model, team_stats) final  # \u0412\u044b\u0432\u043e\u0434 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 print(\"1\/8 \u0444\u0438\u043d\u0430\u043b\u0430 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b:\") for (i in 1:nrow(round_of_16)) {   print(paste(round_of_16$home_team[i], \"vs\", round_of_16$away_team[i], \"-> Prediction:\", round_of_16_results[i])) }  print(\"1\/4 \u0444\u0438\u043d\u0430\u043b\u0430 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b:\") for (i in 1:nrow(quarterfinals)) {   print(paste(quarterfinals$home_team[i], \"vs\", quarterfinals$away_team[i], \"-> Prediction:\", quarterfinal_results[i])) }  print(\"1\/2 \u0444\u0438\u043d\u0430\u043b\u0430 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b:\") for (i in 1:nrow(semifinals)) {   print(paste(semifinals$home_team[i], \"vs\", semifinals$away_team[i], \"-> Prediction:\", semifinal_results[i])) }  print(\"\u0424\u0438\u043d\u0430\u043b \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442:\") print(paste(final$home_team, \"vs\", final$away_team, \"-> Prediction:\", final_result)) <\/code><\/pre>\n<p>1\/8 \u0444\u0438\u043d\u0430\u043b\u0430:<\/p>\n<p>[1] &#171;Switzerland vs Italy -> Prediction: Away Win&#187;<\/p>\n<p>[1] &#171;Germany vs Denmark -> Prediction: Away Win&#187;<\/p>\n<p>[1] &#171;England vs Slovakia -> Prediction: Home Win&#187;<\/p>\n<p>[1] &#171;Spain vs Georgia -> Prediction: Home Win&#187;<\/p>\n<p>[1] &#171;France vs Belgium -> Prediction: Home Win&#187;<\/p>\n<p>[1] &#171;Portugal vs Slovenia -> Prediction: Home Win&#187;<\/p>\n<p>[1] &#171;Romania vs Netherlands -> Prediction: Away Win&#187;<\/p>\n<p>[1] &#171;Austria vs Turkey -> Prediction: Away Win&#187;<\/p>\n<p>\u041f\u0440\u043e\u0446\u0435\u043d\u0442 \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u0438 \u0432\u044b\u0440\u043e\u0441 &#8212; &#171;Accuracy: 0.724324324324324&#187;. \u041b\u043e\u0433\u0438\u0447\u043d\u043e, \u0443\u0447\u0438\u0442\u044b\u0432\u0430\u044f \u0438\u0433\u0440\u0443 \u043d\u0430 \u0434\u0432\u0430 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0430. <\/p>\n<p>\u0414\u0430\u043b\u0435\u0435 \u0431\u0443\u0434\u0443 \u0434\u0430\u0432\u0430\u0442\u044c \u043f\u0440\u043e\u0433\u043d\u043e\u0437 \u0432 \u043a\u043e\u043c\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u044f\u0445 \u0432 \u0437\u0430\u0432\u0438\u0441\u0438\u043c\u043e\u0441\u0442\u0438 \u043e\u0442 \u0440\u0435\u0430\u043b\u044c\u043d\u043e \u0441\u043b\u043e\u0436\u0438\u0432\u0448\u0438\u0445\u0441\u044f \u043f\u0430\u0440.<\/p>\n<p>\u0421\u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0439 \u0444\u0438\u043d\u0430\u043b: &#171;England vs Spain -> Prediction: Away Win&#187;<\/p>\n<p>\u041f\u043e\u0431\u0435\u0434\u0438\u0442\u0435\u043b\u0435\u043c \u0441\u043d\u043e\u0432\u0430 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0430 \u0418\u0441\u043f\u0430\u043d\u0438\u044f, \u043a\u043e\u044d\u0444\u0444\u0438\u0446\u0438\u0435\u043d\u0442 \u043d\u0430 \u043f\u043e\u0431\u0435\u0434\u0443 \u0441\u043d\u0438\u0437\u0438\u043b\u0441\u044f \u0441 9.00 \u0434\u043e 5.50.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<p><!----><!----><\/div>\n<p><!----><!----><br \/> \u0441\u0441\u044b\u043b\u043a\u0430 \u043d\u0430 \u043e\u0440\u0438\u0433\u0438\u043d\u0430\u043b \u0441\u0442\u0430\u0442\u044c\u0438 <a href=\"https:\/\/habr.com\/ru\/articles\/825510\/\"> https:\/\/habr.com\/ru\/articles\/825510\/<\/a><br \/><\/br><\/br><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[],"tags":[],"class_list":["post-400015","post","type-post","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts\/400015","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=400015"}],"version-history":[{"count":0,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=\/wp\/v2\/posts\/400015\/revisions"}],"wp:attachment":[{"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=400015"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=400015"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/savepearlharbor.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=400015"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}