In scikit-learn, the feature_importances_ attribute is associated with tree-based models, such as Decision Trees, Random Forests, and Gradient Boosted Trees. This attribute provides a way to assess the importance of each feature (or variable) in making predictions with the trained model. When you train a tree-based model, the algorithm makes decisions at each node based…
To visualize a decision tree in scikit-learn, you can use the plot_tree function from the sklearn.tree module. This function allows you to generate a visual representation of the decision tree. Here’s a simple example: To show the decision tree as text in scikit-learn, you can use the export_text function from the sklearn.tree module. This function…
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:
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…
