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Some of the model widgets are:
• Tree Widget: It is used to create a decision tree model.
• Random Forest Widget: It creates an ensemble of decision trees (a forest of trees).
Instead of using just one decision tree, it combines multiple trees to make more accurate
predictions.
• Linear Regression Widget: It is used to create a linear regression model, which predicts
a continuous value based on input data.
• Logistic Regression Widget: It is used for classification tasks where the goal is to
predict one of two possible outcomes (e.g., Yes/No or True/False).
Evaluate Widget
Evaluate Widget is used to test and assess how well your machine learning model is performing. After you’ve
created and trained a model, it’s important to evaluate its accuracy and effectiveness.
Some of the Evaluate widgets are:
• Test and Score Widget: It helps to evaluate the performance of a model by testing it on unseen data
(data that wasn’t used in training). This widget calculates various performance metrics such as accuracy,
precision, recall, and more.
• Predictions Widget: It shows how the model makes predictions
on new, unseen data. It provides predicted labels or values for the
test data and compares them with the actual values to assess the
model’s accuracy.
• Confusion Matrix Widget: It displays a table that compares
predicted and actual class labels, making it easier to understand
classification model performance.
• ROC Analysis Widget: It plots the ROC curve, comparing the
True Positive Rate against the False Positive Rate, helping assess
classification model performance.
Unsupervised Widgets
Unsupervised widgets help to apply unsupervised learning models
to our data and visualise it. Unsupervised learning refers to the type
of machine learning where the model is given data without labeled
outcomes (i.e., no target variable). This will help in finding the hidden
patterns or structure in the data.
Some of the Unsupervised widgets are:
• Distance Matrix Widget: It calculates and visualises the distances or
similarities between data points.
• t-SNE Widget: Reduces dimensionality and visualises high-dimensional
data in 2D/3D.
• Correlations Widget: Calculates correlations between features to
show relationships.
• K-Means Widget: K-means is one of the most popular clustering
algorithms. It is used to group data points into clusters based on their
similarity.
270 Touchpad Artificial Intelligence (Ver. 3.0)-X

