Page 272 - AI Ver 3.0 class 10_Flipbook
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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.


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