LightGBM, short for Light Gradient Boosting Machine, is an open-source framework for gradient boosting developed by Microsoft that is highly efficient, flexible, and widely used in the machine learning field. It uses decision tree-based learning algorithms and follows a leaf-wise approach with depth-wise pruning, unlike traditional gradient boosting methods which usually work in a level-wise approach.

A key concept for understanding LightGBM is gradient boosting. Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.

Gradient Boosting model is built in a stage-wise fashion and it is generalized by allowing an arbitrary differentiable loss function. If we consider L as our loss function and y as the actual output, for a given iteration i, the model is defined by:

F(i)(x) = F(i-1)(x) + argmin(h) [L(y, F(i-1)(x) + h(x))]

F(i)(x) is the boosted model at iteration i
h(x) is a base (weak) learner

In each iteration of gradient boosting, a new weak learner is fit on a modified version of the original dataset.

Now, the unique features of LightGBM are the following:

Leaf-wise Tree Growth: LightGBM optimizes the tree learning algorithm by choosing to split the leaf that will result in the largest decrease in the loss function, instead of splitting level-wise. This can result in increased accuracy, but it can also cause overfitting on small datasets.

Histogram-based Algorithm: Instead of using pre-sorted algorithms for decision tree learning, LightGBM uses histogram-based algorithms that bucket continuous feature (attribute) values into discrete bins, which speeds up the training process and reduces memory usage.

Gradient-based One-Side Sampling (GOSS): To handle large datasets, LightGBM includes a novel sampling technique that selects instances for the negative gradients (underestimated instances) and a random selection of instances with positive gradients (overestimated instances). This helps to maintain the balance of data distribution while making training faster.

Exclusive Feature Bundling (EFB): LightGBM reduces the dimension of feature space by bundling exclusive features (features that are not simultaneously nonzero) without significant loss of information. This is especially effective in case of high dimensional sparse data.

Remember, gradient boosting, and by extension LightGBM, is a complex machine learning technique that involves many different hyperparameters and design choices. Choosing the right configuration for your problem can involve a lot of experimentation and fine-tuning.