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.
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…
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…
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 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…
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.…
PolynomialFeatures is a preprocessing technique used in machine learning, particularly in polynomial regression. It transforms an input feature matrix by adding new features that are polynomial combinations of the original features. For example, if you have a feature (x), PolynomialFeatures can generate additional features like , etc., up to a specified degree. This allows the…
nivariate exploration refers to the analysis of a single variable in isolation. In data analysis, univariate exploration involves examining the distribution, central tendency, and variability of a single variable without considering its relationship with other variables. Common techniques used in univariate exploration include: Univariate exploration is often the first step in data analysis, providing insights…
The steps to follow to use machine learning models are: In “fit” and “predict” steps, you can use several models, and evaluate them, to keep the most performing one. Python libraries: Here, we train a model to guess a comfortable boot size for a dog, based on the size of the harness that fits them:…
Feature scaling is considered a part of the data processing cycle that cannot be skipped, so that we can achieve stable and fast training of our ML algorithm. eature Scaling is a technique to standardize the independent features present in the data in a fixed range. It is performed during the data pre-processing to handle…
