To understand it, I will share the formula of simple linear regression and briefly explain the role of coefficients B0 and B1. linear regression formula: y = β0 + β1 ⋅ X+ϵ y is the variable we want to predict (salary) ...
Gradient boosting machines(GBMs) are currently very popular and so it's a good idea for machine learning practitioners to understand how GBMs work. The problem is that understanding all of the mathematical machinery is tricky and, unfortunately, these details are needed to tune the hyper-paramete...
A collection of practical tips and tricks to improve the gradient descent process and make it easier to understand.Other articles from this series Introduction to machine learning — What machine learning is about, types of learning and classification algorithms, introductory examples. Linear regression...
(一)How to Do Linear Regression using Gradient Descent 1、解决问题 The optimal values of m and b can be actually calculated with way less effort than doing a linear regression. this is just to demonstrate gradient descent 2、数据介绍 3、代码 4、出处... ...
Duringgradient descent, we use the gradient of a loss function (the derivative, in other words) to improve the weights of a neural network. To understand the basic concept of the gradient descent process, let’s consider a basic example of a neural network consisting of only one input and ...
How to Configure Gradient Boosting Machines In the 1999 paper “Greedy Function Approximation: A Gradient Boosting Machine“, Jerome Friedman comments on the trade-off between the number of trees (M) and the learning rate (v): The v-M trade-off is clearly evident; smaller values of v give...
I've been using h2o.gbm for a classification problem, and wanted to understand a bit more about how it calculates the class probabilities. As a starting point, I tried to recalculate the class probability of a gbm with only 1 tree (by looking at the observations in the leafs), but the...
What is the right way to conduct example weighting? How do you understand loss functions and so-called theorems on them? The importance of example weighting Focusing on informative examples; Addressing Label Noise Addressing sample imbalance (long-tailed data distribution) ...
I try to understand why I obtain different metrics using “model.evaluate” vs “model.predict” and then compute the metrics… I work on sementic segmentation. I have an evaluation set of 24 images. I have a custom DICE INDEX metrics defined as : ” def dice_coef(y_true, y_pred...
Theforwardmethod defines the forward pass of a neural network. It essentially specifies how input data should be processed through the different layers of the network during training and inference to produce an output. Let’s walk through the different parts of the model architecture to u...