Description  
The objective of the competition is to help us build as good a model as possible so that we can, as optimally as this data allows, relate molecular information to an actual biological response. We have shared the data in the comma-separated values (CSV) format. Each row in this data set represents a molecule. The first column contains experimental data describing an actual biological response; the molecule was seen to elicit this response (1), or not (0). The remaining columns represent molecular descriptors (d1 through d1776). These are calculated properties that can capture some of the characteristics of the molecule - for example, size, shape, or elemental constitution. The descriptor matrix has been normalized.

Evaluation  
Predicted probabilities that a molecule elicits a response are evaluated using the log loss metric.  
Log loss is defined as:

\[ \text{log loss} = -\frac{1}{N} \sum_{i=1}^N \left[ y_i \log(\hat{y_i}) + (1 - y_i) \log(1 - \hat{y_i}) \right] \]

where \( N \) is the number of samples, \(\text{log}\) is the natural logarithm, \(\hat{y_i}\) is the posterior probability that the \( i^{th} \) sample elicited a response, and \( y_i \) is the ground truth (\( y_i = 1 \) means the molecule elicited a response, \( y_i = 0 \) means that it did not).

Dataset Description  
The data is in the comma-separated values (CSV) format. Each row in this data set represents a molecule. The first column contains experimental data describing a real biological response; the molecule was seen to elicit this response (1), or not (0). The remaining columns represent molecular descriptors (d1 through d1776). These are calculated properties that can capture some of the characteristics of the molecule - for example, size, shape, or elemental constitution. The descriptor matrix has been normalized.