Browsed by
Category: Machine learning

Machine Learning: Predicting Values with Multiple Models- Part II

Machine Learning: Predicting Values with Multiple Models- Part II

In my previous post, I predicted grain weight from length and width of grains using Random Forest. ■ Machine Learning: Predicting Values with Multiple Models- Part I Now, my next question is how the model accuracy changes when grain area and genotype are added. If you followed my previous post closely, you should be able to understand the code below. ■ Data upload ■ Data Splitting Unlike the previous data, I have now added genotype and grain area to the model. ■ Machine…

Read More Read More

Machine Learning: Predicting Values with Multiple Models- Part I

Machine Learning: Predicting Values with Multiple Models- Part I

Machine learning (ML) is a field of artificial intelligence (AI) that enables computers to learn from and make predictions or decisions based on data. Rather than being explicitly programmed to perform a specific task, ML algorithms use data to identify patterns and make inferences or predictions. Machine Learning can be divided into supervised and unsupervised learning. In supervised learning, the model is trained on labeled data, which means the input data is paired with the correct output, and it can…

Read More Read More

Machine Learning: How to Perform Classification with Different Models?

Machine Learning: How to Perform Classification with Different Models?

Machine learning (ML) is a field of artificial intelligence (AI) that enables computers to learn from and make predictions or decisions based on data. Rather than being explicitly programmed to perform a specific task, ML algorithms use data to identify patterns and make inferences or predictions. What is Classification in Machine Learning? Classification is a type of supervised learning where the goal is to categorize data into predefined classes. For example, classifying emails as “spam” or “not spam.” Different models…

Read More Read More

Machine Learning: Modeling with Random Forest Using Python

Machine Learning: Modeling with Random Forest Using Python

In my previous post, I introduced stepwise regression to select the best model. I suggested that grain yield = -4616.47 + 10.53 * stem biomass + 41.03 * height, indicating that stem biomass and height are the most important variables affecting grain yield. ■ Stepwise Regression: A Practical Approach for Model Selection using R Now, I’ll find the best model using machine learning. This is a small dataset, which might not be suitable for machine learning, but it serves as…

Read More Read More

How to Sample a Portion of Data using R?

How to Sample a Portion of Data using R?

Comments 0 Comment

I have one big dataset. Let’s upload to R. This data has 96,319 data rows. I want to use some part of this data. How can I randomly extract some data from the whole dataset. First, I’ll add number from 1 to the end of the data row to provide ID of each data row. Caret package The caret package (short for Classification And REgression Training) is a set of functions that attempt to streamline the process for creating predictive models. You can find…

Read More Read More

Exploring Machine Learning Fundamentals: Predicting Survival on the Titanic

Exploring Machine Learning Fundamentals: Predicting Survival on the Titanic

Comments 0 Comment

In 2024, one of my goals is to learn machine learning and publish a crop physiology paper in an academic journal using machine learning. While taking online or offline courses of machine learning, I discovered Kaggle, a popular platform for data science and machine learning competitions, datasets, and tutorials. Kaggle provides excellent datasets for practicing basic machine learning and data analysis. If you visit the Kaggle website: Titanic – Machine Learning from Disaster, you can access and download various datasets….

Read More Read More