What is a Gradient-based Strategy

Gradient-based strategy is commonly known as Gradient boosting which is a fundamental machine learning technique used by many gradient boosting algorithms like LightGBM to optimize and enhance the performance of predictive models. In a gradient-based strategy, multiple weak learners(commonly Decision trees) are combined to achieve a high-performance model. There are some key processes associated with a gradient-based strategy which are listed below:

• Gradient Descent: In the gradient-based strategy, the optimization algorithm (usually gradient descent) is used to minimize a loss function that measures the difference between predicted values and actual target values.
• Iterative Learning: The model iteratively updates its predictions for each step by calculating gradients (slopes) of the loss function with respect to the model’s parameters. These gradients are calculated to know the right way to minimize the loss.
• Boosting: In gradient boosting, weak learners (decision trees) are trained sequentially where each tree attempting to correct the errors made by the previous ones and the final prediction is the combination of predictions from all the trees.

Benefits of Gradient-based strategy

We can get several benefits in our predictive model if we utilize gradient-based strategy which are listed below:

1. Model Accuracy: Gradient boosting, including LightGBM, is known for its high predictive accuracy which is capable to capture complex relationships in the data by iteratively refining the model.
2. Robustness: The ensemble nature of gradient boosting makes it robust against overfitting problem. Each new tree focuses on the mistakes of the previous trees which reduces the risk of capturing noise in the data.
3. Flexibility: Gradient boosting has in-build mechanism to handle various types of data including both numerical and categorical features which makes it suitable for a wide range of machine learning tasks.
4. Interpretability: While ensemble models can be complex but they can offer interpretability through feature importance rankings which can be used in conjunction with interpretability tools like SHAP values to understand model decisions.

LightGBM is a well-known high-performing model that uses a gradient-based strategy in its internal training process. Gradient-based strategy effectively enhances a model to make it highly optimized, accurate in prediction, and memory efficient which unlocks an easy way to handle complex and large real-world datasets used in various machine learning tasks. In this article, we will see the implementation of LightGBM and then visualize how its gradient-based strategy works on each feature of the dataset.