Machine Learning Brainweights

Mar 17

Improving accuracy of machine learning models. How ?

Fitting a machine learning model can be as simple as A, B, C, D but how to make that machine learning model predict accurately with as… — Fitting a machine learning model can be as simple as A, B, C, D but how to make that machine learning model predict accurately with as minimal error as possible is the most difficult part as data scientist hence the motivation to write this. This blog post will purpose to…

Machine Learning

7 min read

Jun 1, 2021

Random forest demystified

What is random forest ? — What is random forest ? Random forest is a supervised machine learning algorithm built on multiple decision trees that ensembles (combines together) trees to improve the output. Random forest is used to solve both regression and classification problems. Click here to read on decision trees to better understand random forest. …

3 min read

Feb 15, 2021

Principal Component Analysis

Before delving deep into the workings of the algorithm, it's vital to get the context right, how principal component comes in and why it… — Before delving deep into the workings of the algorithm, it's vital to get the context right, how principal component comes in and why it is essential when it comes to machine learning. Assuming you are a car dealer and you were to evaluate the value of a vehicle depending on…

4 min read

Dec 19, 2019

Getting started on decision trees

machine learning brain weights blog — how decision trees work,training a decision tree model,visualizing and making predictions “What exactly are decision trees?” A decision tree is a versatile machine learning algorithm that emulates a flowchart like structure containing branches that continuously splits data into smaller sets showing all possible outcomes of an event and can be…

Machine Learning

5 min read