LightGBM represents a framework for the light gradient to enhance memory utilization and productivity based on the decision trees algorithm. LightGBM works on Windows, Linux, and macOS and supports Python, C, R, and C#. LightGBM uses two new techniques, and those are :
The primary difference between lightGBM vs. XGboost is that the LightGBM approach filters data instances to determine a divided value through the GOSS technique, while XGBoost uses a histogram-based algorithm and a pre-sorted algorithm for the most efficient division calculation. Instead, LightGBM uses a highly optimized histogram-based decision-making algorithm, which provides both efficiency and memory consumption along with its considerable advantages.
In the calculation of the information gain, many different data instances have various responsibilities. Large gradients instances tend to add more to the information gain. For lightGBM example, the greater than or between the top percentiles are above the predefined thresholds to drop randomly. These instances with the small gradients are the limitations of accuracy in order to preserve the data-gain estimate. This process should lead to a more precise in estimating the gain with the same rate of sampling than random sampling, particularly if the gain in the information is of a wide range.
Input:d: iterations, I: training data
Input: a: large gradient data sampling ratio
Input: b: small gradient sample data
Input: L: low learner, loss: loss function
models ? {}, randN ? b × len(I), fact ? (1-a)/b
topN ? a × len(I)
for i = 1 to d do
preds ? w ? {1, 1, …}, models.predict(I) g ? loss(I, preds).
sorted ? Get Sorted Indices (abs(g))
topSet ? sorted [1:topN]
randSet ? Random Pick usedSet ? topSet randSet
w[randSet] × = fact . Assign weight f act to the
small gradient data.
newModel, g[usedSet], w[usedSet])
models.append(newModel)
As per GBMÂ machine learning, let us see the GOSS technique mathematical analysis.
For a training set in which each xi is in space Xs, in case n {x1,···, xn}, a vector in s. The negatives of the loss function in relation to output in the GBM model are referred to as {g1, · · ·, gn} in every iteration of gradient boost. In this GOSS LightGBM technique, the training sessions are classified in descending order as per their absolute values. Then, the top cases with larger gradients are retained, and the instance subset A comes in. Finally, we divide the cases by estimated VJ(d) variance gain over the sub-set A? B.
where and the coefficient (1-a)/b is used to for the sum of the gradients and Al = {xi ? A : xij ? d}, Bl = {xi ? B : xij ? d}, Ar = {xi ? A : xij > d}, Br = {xi ? B : xij > d}.
Usually, high-dimensional information is very small, which allows us to design an almost non-loss approach to minimize the number of characteristics. There are many mutually exclusive features in sparse function space, which means that non-zero values are never taken simultaneously. The exclusive features should be securely integrated into one feature. Â Therefore, speed is increased without harming accuracy for the training framework.
Input: numData: number of data
Input: F: One bundle of exclusive features
binRanges ? {0}, totalBin ? 0
for f in F do
    totalBin = f.numBin
  binRanges
newBin ? new Bin(numData)
for i = 1 to numData do
    newBin[i] ? 0
for j = 1 to len(F) do
if F[j].bin[i] is 0 then new F[j].bin[i]? Bin[i], bin Ranges[j]
Output: newBin, binRanges
According to other boosting algorithms, the LightGBM classifier has the tree from the leaf point of view. LightGBM selects the leaf to rise with the highest delta loss. The leaf’s lightGBM algorithm is weaker than the level algorithm because of its fixation. The growth in the leafy tree should improve the model’s complications and cause small data sets to overfit.
The following are a few critical parameters, and their usages are described below:Â
I hope the above article has a detailed overview of LightGBM and how lightGBM works.
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