:clipboard:Decision trees and random forests are powerful and widely used machine learning models.
:world_map:The training, validation, and test split is essential for evaluating and reporting model accuracy.
:floppy_disk:Separating the input and target columns is crucial for effective machine learning.
:bar_chart:Exploratory data analysis can help identify important columns for prediction.
:computer:Web scraping can be used to collect additional data for training machine learning models.
What are some examples of hyper-parameters?
—Examples of hyper-parameters include the maximum depth of a decision tree, the number of trees in a random forest, and the learning rate of a gradient boosting algorithm.
How do decision trees and random forests differ?
—A decision tree consists of a single tree-like model, while a random forest is an ensemble of multiple decision trees. Random forests are generally more accurate but can be slower to train.
What is the purpose of the training, validation, and test split?
—The training data is used to train the model, the validation data is used to evaluate different versions of the model, and the test data is used to report the final accuracy of the model.
Why is it important to separate the input and target columns?
—Separating the input and target columns ensures that the model is not trained to predict the target column using the target column itself. This helps prevent overfitting and provides meaningful predictions.
How can web scraping be used to collect data for machine learning?
—Web scraping involves extracting data from websites. By scraping relevant websites, you can gather additional data to improve the performance of your machine learning model.
00:00Introduction to the Machine Learning with Biotin Zero to GB course
08:42Downloading and preparing the dataset for training
13:36Creating a training, validation, and test split
16:12Identifying the input and target columns
17:41Importance of separating the input and target columns
