{"id":5397,"date":"2021-04-22T17:39:08","date_gmt":"2021-04-22T15:39:08","guid":{"rendered":"https:\/\/joapen.com\/blog\/?p=5397"},"modified":"2021-05-04T14:06:40","modified_gmt":"2021-05-04T12:06:40","slug":"introduction-to-machine-learning-in-kaggle","status":"publish","type":"post","link":"https:\/\/joapen.com\/blog\/2021\/04\/22\/introduction-to-machine-learning-in-kaggle\/","title":{"rendered":"Introduction to Machine Learning in Kaggle"},"content":{"rendered":"\n<p>I&#8217;m going through the course &#8220;<a rel=\"noreferrer noopener\" href=\"https:\/\/www.kaggle.com\/learn\/intro-to-machine-learning\" target=\"_blank\">Intro to Machine Learning<\/a>&#8220;, and I would like to  keep some notes about it.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">My first machine learning code<\/h2>\n\n\n\n<pre class=\"wp-block-code\"><code># Code you have previously used to load data\nimport pandas as pd\n\n# Path of the file to read\niowa_file_path = '..\/input\/home-data-for-ml-course\/train.csv'\n\nhome_data = pd.read_csv(iowa_file_path)\n\n# Set up code checking\nfrom learntools.core import binder\nbinder.bind(globals())\nfrom learntools.machine_learning.ex3 import *\n\n# print the list of columns in the dataset to find the name of the prediction target\nhome_data.columns\n\n# Set the target (price)\ny = home_data.SalePrice\n\n# Create the list of features below\nfeature_names = &#91;'LotArea', 'YearBuilt','1stFlrSF','2ndFlrSF','FullBath', 'BedroomAbvGr', 'TotRmsAbvGrd']\n\n# Select data corresponding to features in feature_names\nX = home_data&#91;feature_names]\n\n# Review data\n# print description or statistics from X\nprint(X.describe)\n\n# print the top few lines\nprint(X.head())\n\n#specify the model. \n#For model reproducibility, set a numeric value for random_state when specifying the model\nfrom sklearn.tree import DecisionTreeRegressor\niowa_model = DecisionTreeRegressor(random_state=1)\n\n# Fit the model\niowa_model.fit(X,y)\n\npredictions = iowa_model.predict(X)\nprint(predictions)\n\n\n<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\">Model validation<\/h2>\n\n\n\n<p>In almost all applications, the relevant measure of model quality is <strong>predictive accuracy<\/strong>. In other words, will the model&#8217;s predictions be close to what actually happens.<\/p>\n\n\n\n<p>Mean Absolute Error&nbsp;(MAE) give us the absolute value of each error.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># Specify the model\niowa_model = DecisionTreeRegressor(random_state=1)\n\n# Fit iowa_model with the training data.\niowa_model.fit(train_X, train_y)\n\n# Predict with all validation observations\nval_predictions = iowa_model.predict(val_X)\n\n# print the top few validation predictions\nprint(val_predictions)\n\nfrom sklearn.metrics import mean_absolute_error\nval_mae = mean_absolute_error(val_y, val_predictions)\n\n# uncomment following line to see the validation_mae\nprint(val_mae)<\/code><\/pre>\n\n\n\n<h4 class=\"wp-block-heading\">Some machine learning metrics:<\/h4>\n\n\n\n<ul class=\"wp-block-list\"><li>Accuracy<\/li><li>Confusion Matrix<\/li><li>Area Under the ROC Curve (AUC)<\/li><li>F1 Score<\/li><li>Precision-Recall Curve<\/li><li>Log\/Cross Entropy Loss<\/li><li>Mean Squared Error<\/li><li>Mean Absolute Error<\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Under-fitting and over-fitting<\/h2>\n\n\n\n<p>Models can suffer from either:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li><strong>Over fitting:<\/strong>&nbsp;capturing spurious patterns that won&#8217;t recur in the future, leading to less accurate predictions, or<\/li><li><strong>Under fitting:<\/strong>&nbsp;failing to capture relevant patterns, again leading to less accurate predictions.<\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">The 7 Steps of Machine Learning<\/h2>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/i.imgur.com\/mqTCqBR.png\" alt=\"\"\/><\/figure>\n\n\n\n<ul class=\"wp-block-list\"><li>Step 1: Gather the data. When participating in a Kaggle competition, this step is already completed for you.<\/li><li>Step 2: Prepare the data &#8211; Deal with missing values and categorical data. (Feature engineering is covered in a separate course.)<\/li><li>Step 4: Train the model &#8211; Fit decision trees and random forests to patterns in training data.<\/li><li>Step 5: Evaluate the model &#8211; Use a validation set to assess how well a trained model performs on unseen data.<\/li><li>Step 6: Tune parameters &#8211; Tune parameters to get better performance from XGBoost models.<\/li><li>Step 7: Get predictions &#8211; Generate predictions with a trained model and submit your results to a Kaggle competition.<\/li><\/ul>\n\n\n\n<h4 class=\"wp-block-heading\">Automated machine learning (AutoML)<\/h4>\n\n\n\n<p>Read how to use\u00a0<a href=\"https:\/\/cloud.google.com\/automl-tables\/docs\/beginners-guide\"><strong>Google Cloud AutoML Tables<\/strong><\/a>\u00a0to automate the machine learning process. While Kaggle has already taken care of the data collection, AutoML Tables will take care of all remaining steps.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" src=\"https:\/\/i.imgur.com\/5SekA3O.png\" alt=\"\"\/><figcaption>Important to read the Google Cloud AutoML Tables<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Course completed!<\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"631\" src=\"https:\/\/joapen.com\/blog\/wp-content\/uploads\/2021\/04\/image-9-1024x631.png\" alt=\"Certificate recognizing that joapen has successfully completed the Kaggle course Intro to Machine Learning taught by Dan Becker on April 22, 2021\" class=\"wp-image-5421\" srcset=\"https:\/\/joapen.com\/blog\/wp-content\/uploads\/2021\/04\/image-9-1024x631.png 1024w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2021\/04\/image-9-300x185.png 300w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2021\/04\/image-9-768x474.png 768w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2021\/04\/image-9.png 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>I&#8217;m going through the course &#8220;Intro to Machine Learning&#8220;, and I would like to keep some notes about it. My first machine learning code Model validation In almost all applications, the relevant measure of model quality is predictive accuracy. In other words, will the model&#8217;s predictions be close to what actually happens. Mean Absolute Error&nbsp;(MAE) &#8230; <a title=\"Introduction to Machine Learning in Kaggle\" class=\"read-more\" href=\"https:\/\/joapen.com\/blog\/2021\/04\/22\/introduction-to-machine-learning-in-kaggle\/\" aria-label=\"Read more about Introduction to Machine Learning in Kaggle\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":5421,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[151],"tags":[213],"class_list":["post-5397","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","tag-kaggle"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Introduction to Machine Learning in Kaggle -<\/title>\n<meta name=\"description\" content=\"I&#039;m going through the course &quot;Intro to Machine Learning&quot;, and I would like to keep some notes about it. 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