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The primary distinction between these two techniques is that Gradient boosting uses Decision Trees as its fixed base estimator, whereas AdaBoost allows us to customize the base estimator to meet our specific needs. Gradient boosting is a method for gradually, additively, and sequentially training multiple models. The way that AdaBoost and Gradient Boosting Algorithms uncover the flaws of weak learners (such as decision trees), is the main distinction between the two techniques. 

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Gradient boosting accomplishes the same task as the AdaBoost model, which detects flaws by employing high-weight data points (y=ax+b+e, where e deserves special attention as the error term). The loss function is a metric used to assess how well the coefficients of the model fit the underlying data. Depending on the goal of our optimization, a logical grasp of the loss function would be necessary. 

The loss function, for instance, would be based on the discrepancy between actual and expected house prices if we were using a regression to try and predict sales prices. Similarly, the loss function would be a gauge of how well our prediction model categorizes subprime loans if we aimed to categorize credit defaults. The ability to optimize a user-specified cost function rather than a loss function, which typically offers less control and does not closely resemble real-world applications, is one of the main reasons to utilize gradient boosting.

What is gradient boosting?

The primary concept underlying this algorithm is to build models in succession while attempting to minimize the flaws of the prior model. But how do we go about that? How can the error be minimized? By basing a new model on the errors or residuals of the prior one, this is accomplished.

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Gradient Boosting Regressor is used when the target column is continuous; Gradient Boosting Classifier is used when the problem is one of classification. Between the two, the “Loss function” is the only distinction. The goal is to use gradient descent to add weak learners and reduce this loss function. Since it is based on a loss function, we will have various loss functions for regression problems, such as Mean Squared Error (MSE), and for classification problems, such as log-likelihood.

Why use gradient boosting?

Gradient boosting’s benefits

High forecast accuracy: Although adopting gradient boosting doesn’t have this benefit, historically speaking, gradient boosting has frequently won different Kaggle events. This may be attributed to the features of gradient boosting, which combine numerous smaller models and rely on collective intelligence to make the final prediction rather than attempting to fit all the data patterns into a single model.

Flexibility: Using different weak learners (not only decision trees), loss functions, and data types (ordinal, continuous, categorical, etc.), gradient boosting is a form of model that can be used for regression or classification problems.

Easily accessible: Whether you are using R (gbm, xgboost, lightgbm), Julia (GradientBoost), or Python (sklearn, xgboost, lightgbm, catboost), there are a variety of modules from which you may apply gradient boosting to your data problem.

Interpretability: When compared to model topologies like neural networks, gradient-boosting machines probably strike a better compromise between complexity and interpretability. Here is a blog post where I go into great detail about how SHAP functions in case you want to read more about interpreting machine learning models in general.

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Course Syllabus

1. CRISP-DM – Project Management Methodology
2. Exploratory Data Analytics (EDA) / Descriptive Analytics or Descriptive Statistics
3. Statistical Data Business Intelligence and Data Visualization
4. Plots & Inferential Statistics
5. Probability Distributions (Continuous & Discrete)
6. Hypothesis Testing – The ‘4’ Must Know Hypothesis Tests
7. Data Mining Supervised Learning – Linear Regression, OLS
8. Predictive Modelling – Multiple Linear Regression
9. Lasso and Ridge Regressions
10. Logistic Regression – Binary Value Prediction, MLE
11. Multinomial Regression and Ordinal Regression & more 

They include training modules for each special technique and have included one for boosting too. Learn about using ensemble approaches to enhance the consistency and precision of decision tree models. In ensemble techniques, bagging and boosting are the go-to methods. In this module, we talk about how Bagging and Boosting methods use parallel and sequential approaches. 

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The outcome of Random Forest, another ensemble technique built utilizing several Decision Trees, is derived by aggregating the findings from these combinations of trees. This section continues with a discussion of the boosting techniques AdaBoost and Extreme Gradient Boosting. Additionally, stacking techniques will be covered. Learn about the algorithms used by many aspiring data scientists to place first in contests like Kaggle, CrowdAnalytix, etc., and to provide unmatched accuracy.

• Overfitting
• Underfitting
• Voting
• Stacking
• Bagging
• Random Forest
• Boosting
• AdaBoost / Adaptive Boosting Algorithm
• Checking for Underfitting and Overfitting in AdaBoost
• Generalization and Regulation Techniques to avoid overfitting in AdaBoost
• Gradient Boosting Algorithm
• Checking for Underfitting and Overfitting in Gradient Boosting
• Generalization and Regulation Techniques to avoid overfitting in Gradient Boosting
• Extreme Gradient Boosting (XGB) Algorithm
• Checking for Underfitting and Overfitting in XGB
• Generalization and Regulation Techniques to avoid overfitting in XGB

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