Table of Contents
How do you optimize machine learning models?
10 Ways to Improve Your Machine Learning Models
- Introduction.
- Studying learning curves.
- Using cross-validation correctly.
- Choosing the right error or score metric.
- Searching for the best hyper-parameters.
- Testing multiple models.
- Averaging models.
- Stacking models.
What is NDCG in machine learning?
After exploring some of the measures, I settled on Normalized Discounted Cumulative Gain or NDCG for short. NDCG is a measure of ranking quality. In Information Retrieval, such measures assess the document retrieval algorithms.
How is NDCG calculated?
NDCG Calculation In words, we first order the list of candidate answers in descending order based on their relevance score. Then we compute another score for each of this word by taking their respective relevance score and dividing it by the log (base 2) of their rank plus 1 (to avoid division by 0).
How do I learn to rank models?
The training data for a learning to rank model consists of a list of results for a query and a relevance rating for each of those results with respect to the query. Data scientists create this training data by examining results and deciding to include or exclude each result from the data set.
How do you optimize a model?
The area of model optimization can involve various techniques:
- Reduce parameter count with pruning and structured pruning.
- Reduce representational precision with quantization.
- Update the original model topology to a more efficient one with reduced parameters or faster execution.
How do you optimize training?
When training for strength, speed, and quickness, you should be resting 3-5 minutes between sets of that specific exercise. To optimize work done in an amount of time, exercises of opposing muscle groups should be superset or done in a circuit to not hinder the main lift.
What is a good NDCG?
8 NDCG is 80\% of the best ranking. This is an intuitive explanation the real math includes some logarithms, but it is not so far from this.
Why is NDCG sometimes used instead of map?
The primary advantage of the NDCG is that it takes into account the graded relevance values. When they are available in the dataset, the NDCG is a good fit. Compared to the MAP metric it does a good job at evaluating the position of ranked items. It operates beyond the binary relevant/non-relevant scenario.
How do you interpret NDCG scores?
en.wikipedia.org/wiki/Discounted_cumulative_gain nDCG is there so that the values fall between 0 and 1 and has “natural” interpretation. If so, the score of 1 means that the order of hits in a search is perfectly ordered by relevance while 0 is the opposite. 0.5 means half the hits are ordered ok.
How do you interpret NDCG?
nDCG calculates the Cumulative Gain of a set of results by summing up the total relevance of each item in the result set. Then, the position of each item is discounted for, meaning the lower a relevant item is in the list, the higher the penalty or the discount that the item contributes to the total score.
What is learning to rank LTR )? Explain the basic method?
Learning to Rank (LTR) is a class of techniques that apply supervised machine learning (ML) to solve ranking problems. The main difference between LTR and traditional supervised ML is this: Traditional ML solves a prediction problem (classification or regression) on a single instance at a time.