{"version":"1.0","provider_name":"KaLabs","provider_url":"https:\/\/karthicklakshmanan.com","author_name":"karthick","author_url":"https:\/\/karthicklakshmanan.com\/index.php\/author\/karthick\/","title":"AI Model Compendium - KaLabs","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"PhIUH70qaS\"><a href=\"https:\/\/karthicklakshmanan.com\/index.php\/knowledge-base\/ai-model-compendium\/\">AI Model Compendium<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/karthicklakshmanan.com\/index.php\/knowledge-base\/ai-model-compendium\/embed\/#?secret=PhIUH70qaS\" width=\"600\" height=\"338\" title=\"&#8220;AI Model Compendium&#8221; &#8212; KaLabs\" data-secret=\"PhIUH70qaS\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script>\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n<\/script>\n","description":"COMPLETE AI MODEL REFERENCE \u2014 Full COMPLETE AI MODEL REFERENCE Concise descriptions, difficulty level, typical uses and example projects for major AI, ML and deep learning models (comprehensive 2025\u2011level list). Jump to: Classical ML Neural Networks Transformers &#038; LLMs Graph &#038; Relational Reinforcement Learning Generative &#038; Diffusion Hybrid &#038; Agentic Tools Classical Machine Learning Models Model Level Description Common Uses \/ Example Projects Linear Regression Beginner Predict continuous targets via linear combination of features; teaches OLS and gradients. House price prediction; sales\/time-series forecasting; energy consumption modeling; baseline regression experiments; feature selection studies. Logistic Regression Beginner Binary classification using sigmoid; outputs probabilities and interpretable coefficients. Spam detection; medical screening; churn prediction; credit default classification; simple NLP classification with bag\u2011of\u2011words. Decision Tree Beginner Hierarchical splits on features producing human\u2011readable rules; easy to visualize. Credit scoring rules; diagnostic flowcharts; interpretable classification demos; feature importance visualizer; teaching decision logic. Random Forest Intermediate Ensemble of randomized trees; reduces variance and overfitting via averaging. Tabular baseline for industry problems; feature importance reports; anomaly detection; ecology \/ bioinformatics classification; model stacking component. Gradient Boosting (XGBoost \/ LightGBM \/ CatBoost) Intermediate Sequentially built trees that focus on correcting prior errors; state\u2011of\u2011the\u2011art for tabular tasks. Kaggle\u2011style tabular pipelines; credit [&hellip;]"}