{"id":2710,"date":"2022-04-20T15:00:43","date_gmt":"2022-04-20T23:00:43","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=2710"},"modified":"2025-04-24T17:17:24","modified_gmt":"2025-04-24T17:17:24","slug":"how-to-compare-two-or-more-experiments-in-comet","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/how-to-compare-two-or-more-experiments-in-comet\/","title":{"rendered":"How to Compare Two or More Experiments in Comet"},"content":{"rendered":"\n<link rel=\"canonical\" href=\"https:\/\/www.comet.com\/site\/blog\/how-to-compare-two-or-more-experiments-in-comet\">\n\n\n\n<p>\nThis article, written by&nbsp;<a href=\"https:\/\/www.linkedin.com\/in\/angelicaloduca\/\">Angelica Lo Duca<\/a>, first appeared on&nbsp;<a href=\"https:\/\/heartbeat.comet.ml\/make-tracking-your-machine-learning-experiments-easy-afad9b9956a\">Heartbeat<\/a>.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>For two months now I have been studying&nbsp;<a href=\"https:\/\/www.comet.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">Comet<\/a>, a platform for tracking and monitoring Machine Learning experiments. And, truth be told, I am truly amazed by the countless features Comet provides.&nbsp;<strong>I can truly say that every time I use the Comet dashboard, I always discover something new<\/strong>.<\/p>\n\n\n\n<p>Today I would like to describe how to compare the results of two (or more) Machine Learning experiments through the graphical interface provided by the platform.<\/p>\n\n\n\n<p>Comparing two or more experiments is really simple: with a few mouse clicks, you can understand which model performs best.<\/p>\n\n\n\n<p>To illustrate the features provided by Comet, I will describe a practical example, which builds four classification models and chooses the best one.<\/p>\n\n\n\n<p>The article is organized as follows:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Loading the dataset<\/li>\n\n\n\n<li>Running the Experiments<\/li>\n\n\n\n<li>Comparing the Experiments in Comet<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h1 class=\"wp-block-heading\" id=\"c765\">Loading the Dataset<\/h1>\n\n\n\n<p>As a use-case dataset, I use the&nbsp;<a href=\"https:\/\/data.world\/exercises\/logistic-regression-exercise-1\" target=\"_blank\" rel=\"noreferrer noopener\">NBA rookie stats<\/a>, provided by data.world. The objective of the task is to use NBA rookie stats to predict if a player will last five years in the league.<\/p>\n\n\n\n<p>Firstly, I load the dataset as a Pandas dataframe:<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">from comet_ml import Experiment\nimport pandas as pd\ndf = pd.<strong>read_csv<\/strong>('nba_logreg.csv')\ndf.head()<\/pre>\n\n\n\n<p>I have also imported the&nbsp;<code>comet_ml<\/code>&nbsp;library for further use. The following figure shows an extract of the dataset:<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"262\" src=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/Screen-Shot-2022-04-20-at-4.29.33-PM-768x262-1.png\" alt=\"\" class=\"wp-image-2711\" srcset=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/Screen-Shot-2022-04-20-at-4.29.33-PM-768x262-1.png 768w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/Screen-Shot-2022-04-20-at-4.29.33-PM-768x262-1-300x102.png 300w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \/><\/figure>\n\n\n\n<p>The dataset contains 1340 rows and 21 columns. I can use all the columns, but the&nbsp;<code>Name<\/code>&nbsp;and the&nbsp;<code>TARGET_5Yrs<\/code>&nbsp;columns, as input feature, and the&nbsp;<code>TARGET_5Yrs<\/code>&nbsp;as target class. Thus, after dropping the&nbsp;<code>Name<\/code>&nbsp;column and all the&nbsp;<code>NaN<\/code>&nbsp;values:<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">df.drop(['Name'], inplace=True, axis = 1)\ndf.dropna(inplace=True)<\/pre>\n\n\n\n<p>and converting&nbsp;<code>TARGET_5Yrs<\/code>&nbsp;column the to integer:<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">df['TARGET_5Yrs'] = df['TARGET_5Yrs'].astype('int')<\/pre>\n\n\n\n<p>I can build the&nbsp;<code>X<\/code>and&nbsp;<code>y<\/code>&nbsp;variables:<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">X = df.drop(['TARGET_5Yrs'], axis=1)\ny = df['TARGET_5Yrs']<\/pre>\n\n\n\n<p>Now, I can scale all the input features in the interval 0\u20131:<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">from sklearn.preprocessing import StandardScaler\nscaler = StandardScaler()\nX[X.columns] = scaler.fit_transform(X[X.columns])<\/pre>\n\n\n\n<p>Finally, I split data in training and test sets:<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">from sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = <strong>train_test_split<\/strong>(X, y, test_size=0.20, random_state=42)<\/pre>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h1 class=\"wp-block-heading\" id=\"6e06\">Running the Experiments<\/h1>\n\n\n\n<p>Now, I am ready to run the experiments. In this article, I do not focus on model optimization. Rather, my objective is to describe how to compare two or more experiments in Comet. For each experiment, I calculate the following evaluation metrics:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>precision<\/li>\n\n\n\n<li>recall<\/li>\n\n\n\n<li>accuracy<\/li>\n\n\n\n<li>f1-score<\/li>\n\n\n\n<li>confusion matrix.<\/li>\n<\/ul>\n\n\n\n<p>I define an auxiliary function, that returns the first four metrics:<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\ndef <strong>get_metrics<\/strong>(y_pred, y_test):\n    metrics = {}\n    metrics['precision'] = precision_score(y_test, y_pred)\n    metrics['recall'] = recall_score(y_test, y_pred)\n    metrics['f1-score'] = f1_score(y_test, y_pred)\n    metrics['accuracy'] =  accuracy_score(y_test, y_pred)\n    return metrics<\/pre>\n\n\n\n<p>Now, I define another function, named run_experiment(), which builds a Comet Experiment, fits the model passed as an argument, calculates the evaluation metrics, and logs them in Comet:<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">from sklearn.metrics import confusion_matrixdef <strong>run_experiment<\/strong>(model, name):\n    experiment = Experiment()\n    experiment.set_name(name)\n\n    with experiment.train():\n        model.fit(X_train, y_train)\n        y_pred = model.predict(X_train)\n\n    with experiment.validate():\n        y_pred = model.predict(X_test)\n        metrics = compute_metrics(y_pred, y_test)\n        experiment.log_metrics(metrics)\n        experiment.log_confusion_matrix(y_test, y_pred)\n\n    experiment.end()<\/pre>\n\n\n\n<p>In the previous example, I have also used the&nbsp;<code>set_name()<\/code>&nbsp;method provided by the Experiment class to set the name of the experiment to a more human-friendly string.<\/p>\n\n\n\n<p>Now I can call the run_experiment() function with different models. Firstly, I test a Random Forest Classifier:<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">from sklearn.ensemble import RandomForestClassifier\nmodel = RandomForestClassifier()\nrun_experiment(model, 'RandomForest')<\/pre>\n\n\n\n<p>Secondly, a Decision Tree Classifier:<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier()\nrun_experiment(model, 'DecisionTree')<\/pre>\n\n\n\n<p>Then, a Gaussian Naive Bayes Classifier:<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">from sklearn.naive_bayes import GaussianNB\nmodel = GaussianNB()\nrun_experiment(model, 'GaussianNB')<\/pre>\n\n\n\n<p>Finally, a K-Nearest Neighbors Classifier:<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">from sklearn.neighbors import KNeighborsClassifier\nmodel = KNeighborsClassifier()\nrun_experiment(model, 'KNeighborsClassifier')<\/pre>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h1 class=\"wp-block-heading\" id=\"0182\">Comparing the Experiments in Comet<\/h1>\n\n\n\n<p>Once I have run all the experiments, I can check the results in Comet. From the main dashboard, I can see the four experiments:<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"712\" height=\"930\" src=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/1_EdatZu0lno6gwPF20nACtA.jpg\" alt=\"\" class=\"wp-image-2712\" srcset=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/1_EdatZu0lno6gwPF20nACtA.jpg 712w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/1_EdatZu0lno6gwPF20nACtA-230x300.jpg 230w\" sizes=\"auto, (max-width: 712px) 100vw, 712px\" \/><\/figure>\n\n\n\n<p>To choose the best model, simply I can click the sort button:<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"724\" height=\"194\" src=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/1_Q1cpJmw6b2PcOyiOjKQuIQ.jpg\" alt=\"\" class=\"wp-image-2713\" srcset=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/1_Q1cpJmw6b2PcOyiOjKQuIQ.jpg 724w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/1_Q1cpJmw6b2PcOyiOjKQuIQ-300x80.jpg 300w\" sizes=\"auto, (max-width: 724px) 100vw, 724px\" \/><\/figure>\n\n\n\n<p>and choose the sorting criteria:<\/p>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"762\" height=\"442\" src=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/1_Jh0-49AZOe-RD9NYP-YyXQ.jpg\" alt=\"\" class=\"wp-image-2714\" srcset=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/1_Jh0-49AZOe-RD9NYP-YyXQ.jpg 762w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/1_Jh0-49AZOe-RD9NYP-YyXQ-300x174.jpg 300w\" sizes=\"auto, (max-width: 762px) 100vw, 762px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><\/figure>\n\n\n\n<p>In my case, I choose to sort according to accuracy. I can also choose to order by ascending or descending order. In my case, I choose the descending order, thus the top model is the best.<\/p>\n\n\n\n<p>If I click on the Experiments Tab, I can select the columns to show by clicking the columns button:<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"86\" src=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/1_h4WC0VxGYvvXPBoKObOXVA-768x86-1.jpg\" alt=\"\" class=\"wp-image-2715\" srcset=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/1_h4WC0VxGYvvXPBoKObOXVA-768x86-1.jpg 768w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/1_h4WC0VxGYvvXPBoKObOXVA-768x86-1-300x34.jpg 300w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \/><\/figure>\n\n\n\n<p>I select only the evaluation metrics to obtain the following table for comparison among experiments:<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"768\" height=\"264\" src=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/1_y6qdrfzjw9ycvN5ZqgZyLA-768x264-1.jpg\" alt=\"\" class=\"wp-image-2716\" srcset=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/1_y6qdrfzjw9ycvN5ZqgZyLA-768x264-1.jpg 768w, https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/1_y6qdrfzjw9ycvN5ZqgZyLA-768x264-1-300x103.jpg 300w\" sizes=\"auto, (max-width: 768px) 100vw, 768px\" \/><\/figure>\n\n\n\n<p>I note that Random Forest is the best algorithm in terms of accuracy.&nbsp;<strong>The extraordinary aspect is this operation is carried out in a few clicks, which in terms of time requires a few seconds.<\/strong>&nbsp;Through Comet you can shorten run time and make decisions faster.<\/p>\n\n\n\n<p>Now that I have chosen the best model, I can move it to production through the use of Comet Registry. But that\u2019s a whole other story\u2026<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h1 class=\"wp-block-heading\" id=\"c444\">Summary<\/h1>\n\n\n\n<p>Congratulations! You have just learned how to compare two or more experiments in Comet! The Comet dashboard is very simple, thus you can choose your best model in just a few clicks!<\/p>\n\n\n\n<p>You can learn more about Comet by reading my previous articles:&nbsp;<a href=\"https:\/\/heartbeat.comet.ml\/how-to-build-a-customized-panel-in-comet-f61d113ae07b\" target=\"_blank\" rel=\"noreferrer noopener\">How to Build a Customized Panel in Comet<\/a>&nbsp;and&nbsp;<a href=\"https:\/\/heartbeat.comet.ml\/hyperparameter-tuning-in-comet-e7aa637f124c\" target=\"_blank\" rel=\"noreferrer noopener\">Hyperparameter Tuning in Comet<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This article, written by&nbsp;Angelica Lo Duca, first appeared on&nbsp;Heartbeat. For two months now I have been studying&nbsp;Comet, a platform for tracking and monitoring Machine Learning experiments. And, truth be told, I am truly amazed by the countless features Comet provides.&nbsp;I can truly say that every time I use the Comet dashboard, I always discover something [&hellip;]<\/p>\n","protected":false},"author":8,"featured_media":2701,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"customer_name":"","customer_description":"","customer_industry":"","customer_technologies":"","customer_logo":"","footnotes":""},"categories":[9],"tags":[],"coauthors":[132],"class_list":["post-2710","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-product"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>How to Compare Two or More Experiments in Comet - Comet<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.comet.com\/site\/blog\/how-to-compare-two-or-more-experiments-in-comet\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How to Compare Two or More Experiments in Comet\" \/>\n<meta property=\"og:description\" content=\"This article, written by&nbsp;Angelica Lo Duca, first appeared on&nbsp;Heartbeat. For two months now I have been studying&nbsp;Comet, a platform for tracking and monitoring Machine Learning experiments. And, truth be told, I am truly amazed by the countless features Comet provides.&nbsp;I can truly say that every time I use the Comet dashboard, I always discover something [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.comet.com\/site\/blog\/how-to-compare-two-or-more-experiments-in-comet\/\" \/>\n<meta property=\"og:site_name\" content=\"Comet\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/cometdotml\" \/>\n<meta property=\"article:published_time\" content=\"2022-04-20T23:00:43+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-04-24T17:17:24+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/Heartbeat_Article.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"627\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Angelica Lo Duca\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@Cometml\" \/>\n<meta name=\"twitter:site\" content=\"@Cometml\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Angelica Lo Duca\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"How to Compare Two or More Experiments in Comet - Comet","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.comet.com\/site\/blog\/how-to-compare-two-or-more-experiments-in-comet\/","og_locale":"en_US","og_type":"article","og_title":"How to Compare Two or More Experiments in Comet","og_description":"This article, written by&nbsp;Angelica Lo Duca, first appeared on&nbsp;Heartbeat. For two months now I have been studying&nbsp;Comet, a platform for tracking and monitoring Machine Learning experiments. 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