[docs] document that pred_early_stop can be used only in normal and raw scores prediction

[StrikerRUS]

Note that early_stop_ is used only in PredictRaw() and Predict() cases:

LightGBM/src/application/predictor.hpp

Lines 71 to 138 in bfb346c

	if (predict_leaf_index) {
	predict_fun_ = [=](const std::vector<std::pair<int, double>>& features,
	double* output) {
	int tid = omp_get_thread_num();
	if (num_feature_ > kFeatureThreshold &&
	features.size() < KSparseThreshold) {
	auto buf = CopyToPredictMap(features);
	boosting_->PredictLeafIndexByMap(buf, output);
	} else {
	CopyToPredictBuffer(predict_buf_[tid].data(), features);
	// get result for leaf index
	boosting_->PredictLeafIndex(predict_buf_[tid].data(), output);
	ClearPredictBuffer(predict_buf_[tid].data(), predict_buf_[tid].size(),
	features);
	}
	};
	} else if (predict_contrib) {
	if (boosting_->IsLinear()) {
	Log::Fatal("Predicting SHAP feature contributions is not implemented for linear trees.");
	}
	predict_fun_ = [=](const std::vector<std::pair<int, double>>& features,
	double* output) {
	int tid = omp_get_thread_num();
	CopyToPredictBuffer(predict_buf_[tid].data(), features);
	// get feature importances
	boosting_->PredictContrib(predict_buf_[tid].data(), output);
	ClearPredictBuffer(predict_buf_[tid].data(), predict_buf_[tid].size(),
	features);
	};
	predict_sparse_fun_ = [=](const std::vector<std::pair<int, double>>& features,
	std::vector<std::unordered_map<int, double>>* output) {
	auto buf = CopyToPredictMap(features);
	// get sparse feature importances
	boosting_->PredictContribByMap(buf, output);
	};
	} else {
	if (is_raw_score) {
	predict_fun_ = [=](const std::vector<std::pair<int, double>>& features,
	double* output) {
	int tid = omp_get_thread_num();
	if (num_feature_ > kFeatureThreshold &&
	features.size() < KSparseThreshold) {
	auto buf = CopyToPredictMap(features);
	boosting_->PredictRawByMap(buf, output, &early_stop_);
	} else {
	CopyToPredictBuffer(predict_buf_[tid].data(), features);
	boosting_->PredictRaw(predict_buf_[tid].data(), output,
	&early_stop_);
	ClearPredictBuffer(predict_buf_[tid].data(),
	predict_buf_[tid].size(), features);
	}
	};
	} else {
	predict_fun_ = [=](const std::vector<std::pair<int, double>>& features,
	double* output) {
	int tid = omp_get_thread_num();
	if (num_feature_ > kFeatureThreshold &&
	features.size() < KSparseThreshold) {
	auto buf = CopyToPredictMap(features);
	boosting_->PredictByMap(buf, output, &early_stop_);
	} else {
	CopyToPredictBuffer(predict_buf_[tid].data(), features);
	boosting_->Predict(predict_buf_[tid].data(), output, &early_stop_);
	ClearPredictBuffer(predict_buf_[tid].data(),
	predict_buf_[tid].size(), features);
	}
	};

[jameslamb]

makes sense to me, thanks

[github-actions]

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