"Regressor" Example Sentences
1. The linear regressor model was used to analyze the data.
2. The Lasso regressor was more effective at reducing the number of variables.
3. The decision tree regressor provided insights into the relationship between variables.
4. The neural network regressor outperformed other models in predicting future values.
5. The gradient boosting regressor algorithm improved model accuracy.
6. The kernel ridge regressor performed well on the non-linear data.
7. The random forest regressor produced stable results in spite of noise in the data.
8. The Bayesian regressor model was used to estimate the coefficients of the variables.
9. The support vector regressor was used to solve the problem of overfitting.
10. The ridge regressor overcame multicollinearity among variables.
11. The Gaussian process regressor made use of prior distribution to make predictions.
12. The ensemble regressor integrated multiple models for a better performance.
13. The generalized linear regressor assumed a specific distribution of dependent variable.
14. The principal component regressor analyzed the data based on the principal components.
15. The regularized regressor controlled the coefficient values to prevent overfitting.
16. The K-nearest neighbor regressor made prediction based on the k closest neighbors.
17. The Poisson regressor model was applied to count data.
18. The multiple output regressor predicted multiple dependent variables.
19. The tree-based regressor computed averages of observations in each leaf node.
20. The piecewise linear regressor fit different regression lines to different parts of the data.
21. The ordinal regressor analyzed ranked data.
22. The kernel density regressor estimated the probability density function.
23. The elastic net regressor combined L1 and L2 regularization.
24. The quantile regressor predicted the conditional quantiles of the dependent variable.
25. The multitask lasso regressor performed regression on multiple related tasks.
26. The factor analysis regressor reduced the number of variables by factor analysis.
27. The Bernoulli regressor was used for binary dependent variables.
28. The robust linear regressor was less sensitive to outliers in the data.
29. The Cox proportional hazards regressor was used in survival analysis.
30. The ridgeCV regressor model automatically selected the best regularization parameter.
Common Phases
1. The linear
regressor predicts the outcome variable based on the input variable;
2. The multiple
regressor model includes several input variables to predict the outcome;
3. The logistic
regressor estimates the probability of a binary outcome;
4. The polynomial
regressor fits a curve to the data points;
5. The ridge
regressor shrinks the regression coefficients to prevent overfitting;
6. The random forest
regressor builds a collection of decision trees to predict the outcome.