Meta-analysis | Statistical charts and diagrams
A forest plot, also known as a blobbogram, is a graphical display of estimated results from a number of scientific studies addressing the same question, along with the overall results. It was developed for use in medical research as a means of graphically representing a meta-analysis of the results of randomized controlled trials. In the last twenty years, similar meta-analytical techniques have been applied in observational studies (e.g. environmental epidemiology) and forest plots are often used in presenting the results of such studies also. Although forest plots can take several forms, they are commonly presented with two columns. The left-hand column lists the names of the studies (frequently randomized controlled trials or epidemiological studies), commonly in chronological order from the top downwards. The right-hand column is a plot of the measure of effect (e.g. an odds ratio) for each of these studies (often represented by a square) incorporating confidence intervals represented by horizontal lines. The graph may be plotted on a natural logarithmic scale when using odds ratios or other ratio-based effect measures, so that the confidence intervals are symmetrical about the means from each study and to ensure undue emphasis is not given to odds ratios greater than 1 when compared to those less than 1. The area of each square is proportional to the study's weight in the meta-analysis. The overall meta-analysed measure of effect is often represented on the plot as a dashed vertical line. This meta-analysed measure of effect is commonly plotted as a diamond, the lateral points of which indicate confidence intervals for this estimate. A vertical line representing no effect is also plotted. If the confidence intervals for individual studies overlap with this line, it demonstrates that at the given level of confidence their effect sizes do not differ from no effect for the individual study. The same applies for the meta-analysed measure of effect: if the points of the diamond overlap the line of no effect the overall meta-analysed result cannot be said to differ from no effect at the given level of confidence. Forest plots date back to at least the 1970s. One plot is shown in a 1985 book about meta-analysis.The first use in print of the expression "forest plot" may be in an abstract for a poster at the Pittsburgh (US) meeting of the in May 1996. An informative investigation on the origin of the notion "forest plot" was published in 2001. The name refers to the forest of lines produced. In September 1990, Richard Peto joked that the plot was named after a breast cancer researcher called Pat Forrest and as a result the name has sometimes been spelled "forrest plot". (Wikipedia).
A Forest Garden With 500 Edible Plants Could Lead to a Sustainable Future | Short Film Showcase
Instead of neat rows of monoculture, forest gardens combine fruit and nut trees, shrubs, herbs, vines and perennial vegetables together in one seemingly wild setting. This type of agroforestry mimics natural ecosystems and uses the space available in a sustainable way. UK-based Martin Craw
From playlist Nature & Environment | National Geographic
We don't know what a tree is (and this video won't tell you)
Offset your carbon footprint with Wren! They'll protect 5 extra acres of rainforest for each of the first 100 people who sign up at https://www.wren.co/join/minuteearth. It turns out that defining what is and isn't a “tree” is way harder than it seems. LEARN MORE ************** To learn m
From playlist This Is Not A Playlist
This video introduces rooted trees and how to define the relationships among vertices in a rooted tree. mathispower4u.com
From playlist Graph Theory (Discrete Math)
Well, we got the first iteration of the tree drawn, but it doesn't look quite right... -- Watch live at https://www.twitch.tv/simuleios
From playlist Huffman forest
Find out why yew trees are often found in church yards. More tree stories at http://www.test-tube.org.uk/trees/index.htm
From playlist Guide to Trees & Plants
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From playlist Be Smart - LATEST EPISODES!
Related Videos: Fenwick tree range queries: https://www.youtube.com/watch?v=RgITNht_f4Q Fenwick tree point updates: https://www.youtube.com/watch?v=B-BkW9ZpKKM Fenwick tree construction: https://www.youtube.com/watch?v=BHPez138yX8 Fenwick tree source code: https://www.youtube.com/watch?v=e
From playlist Data structures playlist
Making some huffman diagrams for my next video! -- Watch live at https://www.twitch.tv/simuleios
From playlist Huffman forest
Statistical Learning: 8.R.2 Random Forests and Boosting
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
Random Forest In R | Random Forest Algorithm | Random Forest Tutorial |Machine Learning |Simplilearn
🔥Artificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=MachineLearning-HeTT73WxKIc&utm_medium=Descriptionff&utm_source=youtube 🔥Professional Certificate Program In AI And Machine Learning: https://www
From playlist Data Science For Beginners | Data Science Tutorial🔥[2022 Updated]
Intro to Machine Learning: Lesson 4
Today we do a deep dive in to feature importance, including ways to make your importance plots more informative, how to use them to prune your feature space, and the use of a "dendrogram" to understand feature relationships. In the second half of the lesson we'll learn about two more real
From playlist Introduction to Machine Learning for Coders
Thousands of Years Ago, This Was a Forest. See What Remains | Short Film Showcase
Many equate the English moors with open grassland and bogs. However, they were not always this way. Once temperate rainforests, the trees were felled and fires swept through the land. Filmmaker Burnham Arlidge envisions a future where the forests might return, teeming with life. ➡ Subscrib
From playlist Nature & Environment | National Geographic
From playlist CS50 Walkthroughs 2012
StatQuest: Random Forests in R
Random Forests are an easy to understand and easy to use machine learning technique that is surprisingly powerful. Here I show you, step by step, how to use them in R. NOTE: There is an error at 13:26. I meant to call "as.dist()" instead of "dist()". The code that I used in this video ca
From playlist Statistics and Machine Learning in R
Do You Want To Build A Forest? || Thomas J Fan
scikit-learn provides two popular ways to build tree ensembles: Gradient Boosting Decision Trees (GBDT) and Random Forests. In version 0.21, scikit-learn introduced its own Histogram-based GBDT inspired by LightGBM. In this talk, we will learn the underpinnings of GBDT and Random Forests,
From playlist Machine Learning
Lesson 6: Practical Deep Learning for Coders 2022
00:00 Review 02:09 TwoR model 04:43 How to create a decision tree 07:02 Gini 10:54 Making a submission 15:52 Bagging 19:06 Random forest introduction 20:09 Creating a random forest 22:38 Feature importance 26:37 Adding trees 29:32 What is OOB 32:08 Model interpretation 35:47 Removing the r
From playlist Practical Deep Learning for Coders 2022
Finding the Tallest Tree: comparing tree-based models
Tree-based models such as decision trees, random forests, and boosted trees provide powerful predictions and are fast to compute. There are many different ways to fit these models in R, including the rpart, randomForest, and xgboost packages. During this talk, we'll examine numerous ways t
From playlist Introduction to Machine Learning
Graph Theory: 36. Definition of a Tree
In this video I define a tree and a forest in graph theory. I discuss the difference between labelled trees and non-isomorphic trees. I also show why every tree must have at least two leaves. An introduction to Graph Theory by Dr. Sarada Herke. Related Videos: http://youtu.be/zxu0dL436gI
From playlist Graph Theory part-7
Applied ML 2020 - 10 - Calibration, Imbalanced data
Class materials at https://www.cs.columbia.edu/~amueller/comsw4995s20/schedule/
From playlist Applied Machine Learning 2020