You work as a machine learning specialist for a security trading trading firm where you are responsible for building a machine learning model that can predict the price movement of a given stock throughout the trading day.
You have produced several models and you now need to select the best model for your machine learning problem.
You are using scikit-learn to implement your evaluation process.
Which validation technique should you use to determine the best model?
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: C.
Option A is incorrect.
k-Fold Cross-Validation is the most used model validation technique.
However, you are working with time series data (you are predicting price movement over time)
Therefore, Time Series Cross-Validation is a better choice.
Option B is incorrect.
Leave-one-out Cross-Validation does not inherently handle time series data.
Time Series Cross-Validation is a better choice.
Option C is correct.
Using the TimeSeriesSplit scikit-learn method for your cross-validation will give you the best results.
You are predicting price movement over time.
Option D is incorrect.
Bayesian optimization is used for optimizing hyperparameters.
Reference:
Please see the Machine Learning Mastery article titled A Gentle Introduction to Model Selection for Machine Learning (https://machinelearningmastery.com/a-gentle-introduction-to-model-selection-for-machine-learning/), and the Machine Learning Mastery article titled A Gentle Introduction to k-fold Cross-Validation (https://machinelearningmastery.com/k-fold-cross-validation/), the Towards Data Science article titled Validating your Machine Learning Model (https://towardsdatascience.com/validating-your-machine-learning-model-25b4c8643fb7), the Towards Data Science article titled Using the latest advancements in deep learning to predict stock price movements (https://towardsdatascience.com/aifortrading-2edd6fac689d), the scikit-optimize page titled skopt.BayesSearchCV (https://scikit-optimize.github.io/stable/modules/generated/skopt.BayesSearchCV.html)
To determine the best model for a machine learning problem, it is important to evaluate the performance of each model on a validation dataset. The choice of validation technique can depend on the specific problem and the data available. Here are the explanations of the given options:
A. k-Fold Cross-Validation (k-Fold CV) using the scikit-learn KFold method: This method involves dividing the data into k equal parts or "folds." One of the folds is used as the validation set, while the remaining k-1 folds are used as the training set. This process is repeated k times, with each fold being used as the validation set exactly once. The average performance of the model across the k-folds is then used as an estimate of its generalization performance.
B. Leave-one-out Cross-Validation (LOOCV) using the scikit-learn LeaveOneOut method: This method is a special case of k-Fold CV where k equals the number of samples in the dataset. This means that each sample is used as the validation set exactly once, and the model is trained on the remaining samples. This method can be computationally expensive, but it provides an unbiased estimate of the model's generalization performance.
C. Time Series Cross-Validation using the scikit-learn TimeSeriesSplit method: This method is used when the data has a temporal component, such as in stock price prediction. In this method, the data is split into a series of consecutive "folds," with each fold representing a fixed time interval. The model is trained on all data up to a certain point in time and validated on data from a later time period. This process is repeated for each fold, with the validation set moving forward in time.
D. Bayesian optimization using the scikit-learn BayesianSearchCV method: This method involves searching for the best hyperparameters of a model using Bayesian optimization. It uses a probabilistic model of the objective function to guide the search and determine the next set of hyperparameters to evaluate. This method can be useful when the search space for hyperparameters is large and complex.
In the given scenario, since the data has a temporal component, option C. Time Series Cross-Validation using the scikit-learn TimeSeriesSplit method would be the best choice for determining the best model for the stock price prediction problem.