Python scikit-learn library for Decision Tree model
For building a decision tree model in scikit-learn (sklearn), you need to import the relevant modules and classes. Here are the main components you’ll typically use:
Import your functions library to a Google Colab notebook
To import a function from a separate Jupyter Notebook (.ipynb file) into another notebook, you can use the %run magic command:
Get a random sample from your dataset
To grab random sample from a dataset in Python, you can use the pandas library. Assuming your dataset is stored in a pandas DataFrame, you can use the sample method to randomly select rows. Here’s an example: In this example, n=5 specifies the number of rows to sample, and random_state is set to ensure reproducibility.
Receiver Operating Characteristic (ROC) and Area Under Curve (AUC)
he term “Receiver Operating Characteristic” (ROC) originated in the field of signal detection theory during World War II. Initially, it was used to analyze and measure the performance of radar receivers. The ROC curve, in Machine Learning, is a graphical representation that illustrates the trade-off between true positive rate (sensitivity) and false positive rate (1…
Classification metrics: Accuracy, Precision, Recall, and F1-score
uppose we have a binary classification problem in which we have to predict two classes: 1 and 0. A machine learning model tends to make some mistakes by incorrectly classifying data points, resulting in a difference between the actual and predicted class of the data point. Four possible scenarios that can happen are: Clearly, we want…
Python warnings module
In Python, the warnings module provides a way to handle warnings emitted by the Python interpreter or third-party libraries. When you use import warnings, you can control how warnings are displayed or handle them programmatically. Here are some common use cases:
Difference between R-square and Adjusted R-square
very time you add an independent variable to a model, the R-squared increases, even if the independent variable is insignificant. It never declines. Whereas Adjusted R-squared increases only when independent variable is significant and affects dependent variable. where: Example 1: Calculate Adjusted R-Squared with sklearn Example 2: Calculate Adjusted R-Squared with statsmodels A sample function to…
Forward Feature Selection using SequentialFeatureSelector
SequentialFeatureSelector is a feature selection technique. It is part of the feature_selection module and is used for selecting a subset of features from the original feature set. This technique follows a forward or backward sequential selection strategy. Here’s a brief overview: SequentialFeatureSelector is often used in conjunction with machine learning models to identify the most…
One-Hot Encoding
One-hot encoding is a technique used in machine learning and data preprocessing to represent categorical variables as binary vectors. In one-hot encoding, each category or label in a categorical variable is represented as a binary vector, where each element corresponds to a unique category. The process involves the following steps: For example, consider a dataset…
Linear regression model coefficients
Model coefficients, also known as regression coefficients or weights, are the values assigned to the features (independent variables) in a regression model. In a linear regression model, the relationship between the input features (X) and the predicted output (y) is represented as: Here: The model coefficients are estimated during the training of the regression model.…