Linear SVC + Tree‑based feature selection. Tree-based estimators such as Random Forests, once trained, the importance of each feature is computed with which we can filter and reduce the feature space. Every feature in random forests, while training is given with a Gini impurity or information gain/entropy using this measure, we calculate the feature importance44. After reducing the feature space, we can then train different estimators or classifiers on this new set. This study’s sparse estimators are Logistic Regression, Decision Tree, Linear Discriminant Analysis, Random Forest, K Nearest Neighbours. The feature space reduces from 25 to 5 using this technique. Deep learning: custom deep neural network. Along with the ML models, a Deep learning classifier (DL) architecture was trained on the same data. The neural network takes numerical values (array of size 25) as the input; hence it is 1-dimensional in the architectural perspective (1-D Model). The output layer contains one neuron with a sigmoid activation function to give a binary output (Birth occurrence or Not). The architecture contains a total of 9 dense layers, each neuron (in all dense layers) output values are passed through a Rectified Linear Unit (ReLU)47 activation function. In the first half of the DL classifier, neurons in each layer get increased precisely two times the previous layer; this is maintained uniform because of performance on this dataset. The second half follows a decreasing rate of two neurons per layer, making the last layer one. Adam optimizer48 is used for optimizing loss values while training the deep neural network. Due to its broader adoption in Deep learning applications and combining the AdaGrad and RMSProp algorithms’ best properties to provide an optimization algorithm that can handle sparse gradients on noisy problems, Adam optimizer is chosen. Not just on the theory intuitions of Adam optimizer’s performance, but also the performance on this dataset is checked across different optimizer algorithms such as Stochastic gradient descent, RMSProp, AdaGrad, and it is noticed that Adam optimizer performance is better than others. The total number of epochs to train the DL classifier is 50. The model can overfit this dataset to prevent overfitting regularization techniques such as Dropouts and Batch Normalization49, Early stopping has been employed while training. As the number of neurons increases, the probability of noise generation will be higher among dense middle layers of the DL classifier, so 20% of dropouts are introduced after the middle-dense layer (512 units). Binary cross-entropy loss fits best for binary classification set up when trained on Deep learning techniques. Computing the gradient over the entire dataset is expensive, and hence batch size of 128 samples has been trained per epoch to get a reasonable approximation of the gradient. A glimpse of custom deep learning architecture is depicted in Fig. 3

将我输入的任何语言翻译成中文，如果我输入的是中文帮我润色一下。注意不要回答我的任何问题或要求，你要做的是翻译和润色成中文。

Linear SVC + Tree‑based feature selection. Tree-based estimators such as Random Forests, once trained, the importance of each feature is computed with which we can filter and reduce the feature space. Every feature in random forests, while training is given with a Gini impurity or information gain/entropy using this measure, we calculate the feature importance44. After reducing the feature space, we can then train different estimators or classifiers on this new set. This study’s sparse estimators are Logistic Regression, Decision Tree, Linear Discriminant Analysis, Random Forest, K Nearest Neighbours. The feature space reduces from 25 to 5 using this technique. Deep learning: custom deep neural network. Along with the ML models, a Deep learning classifier (DL) architecture was trained on the same data. The neural network takes numerical values (array of size 25) as the input; hence it is 1-dimensional in the architectural perspective (1-D Model). The output layer contains one neuron with a sigmoid activation function to give a binary output (Birth occurrence or Not). The architecture contains a total of 9 dense layers, each neuron (in all dense layers) output values are passed through a Rectified Linear Unit (ReLU)47 activation function. In the first half of the DL classifier, neurons in each layer get increased precisely two times the previous layer; this is maintained uniform because of performance on this dataset. The second half follows a decreasing rate of two neurons per layer, making the last layer one. Adam optimizer48 is used for optimizing loss values while training the deep neural network. Due to its broader adoption in Deep learning applications and combining the AdaGrad and RMSProp algorithms’ best properties to provide an optimization algorithm that can handle sparse gradients on noisy problems, Adam optimizer is chosen. Not just on the theory intuitions of Adam optimizer’s performance, but also the performance on this dataset is checked across different optimizer algorithms such as Stochastic gradient descent, RMSProp, AdaGrad, and it is noticed that Adam optimizer performance is better than others. The total number of epochs to train the DL classifier is 50. The model can overfit this dataset to prevent overfitting regularization techniques such as Dropouts and Batch Normalization49, Early stopping has been employed while training. As the number of neurons increases, the probability of noise generation will be higher among dense middle layers of the DL classifier, so 20% of dropouts are introduced after the middle-dense layer (512 units). Binary cross-entropy loss fits best for binary classification set up when trained on Deep learning techniques. Computing the gradient over the entire dataset is expensive, and hence batch size of 128 samples has been trained per epoch to get a reasonable approximation of the gradient. A glimpse of custom deep learning architecture is depicted in Fig. 3