I've used several of these regression models and will share what I found Industry Email List to be true, which will give you a head start on where to start looking. Multiple regression involves using certain independent variables (rather than just one, as in the example above), to predict a dependent variable. With Google Ads, I've found that there's always an independent variable that's the best predictor of conversions. You probably Industry Email List could have guessed which one it is already. When running ML models on daily labeled training data to predict whether certain features would result in a conversion,
We continually found that all Industry Email List things being equal, campaign spend is the strongest predictor of conversion volume. The following table shows the “root mean squared error” (RMSE) for different ML models. RMSE is an error measure, it shows how far the fitted model is from the training data. The lower the error, the better - it means the Industry Email List model fits the data more accurately. (2) All features include: day of the week, keyword, CTR, Industry Email List CPC, device, final URL (landing page), ad position and cost. We ran five different machine learning algorithms: Decision Tree, K Nearest Neighbors,
Linear Regression, Random Forest, Industry Email List and Support Vector Regression. In most cases, removing "cost" as a feature in the dataset increased the error value more than removing any other feature. This means that the model has become less accurate in predicting the correct outcome. We can also analyze the importance of the features Industry Email List used by the random forest (the best model). It is clear that cost is the key characteristic that the algorithm uses to