• Column effect importance

  Column effect importance in machine learning refers to the measurement of the effect of each feature (column) in a dataset on the outcome being predicted. The purpose is to understand how each feature influences the prediction and to identify which features have the greatest impact on the model's accuracy. For example, consider a dataset that contains information about marketing and sales. The columns might include the number of clicks, the gender of customers, the location, and the facebook caPopular

• Column to predict

  In machine learning, the column to predict, also known as the target variable or response variable, is the column whose value is being predicted by the model. The target variable is dependent on the input features and the goal of the model is to learn the relationship between the input features and the target variable in order to make accurate predictions. For example, in a supervised learning problem, the input features may be age, income, education, and occupation, and the target variable mPopular

• Model summary

  Model Summary displays the accuracy of your model. Accuracy in machine learning refers to the percentage of correct predictions made by a model on a dataset, and it is a classification metrics. Improving accuracy involves finding ways to make the model better at predicting correct outcomes. This can be achieved through several methods, including: Collecting more training data. Using more advanced algorithms or models. Feature Engineering (i.e. creating new features or modifying existing fSome readers

• Columns to ignore

  In machine learning, although it is not suggested to remove columns unless you are confident that the removed columns will not affect the result, you might still find it necessary to ignore certain columns (features) from the dataset for several reasons: Irrelevant columns: Some columns may contain information that is not relevant to the problem being solved and can potentially harm the model's performance. For example, in a model that predicts the likelihood of a person being approved for aSome readers

• Training Algorithms

  Select the algorithms to be used to train your model. Baseline Linear Decision Tree Random Forest Extra Trees LightGBM Xgboost CatBoost Neural Network Nearest NeighborsFew readers

• Optimization metrics

  An optimization metric in machine learning refers to a measure of performance used to guide the training process of a model and evaluate its quality. The optimization metric is used to compare different models and determine the best one for a particular problem. For example, in a binary classification problem, the optimization metric may be accuracy, which measures the proportion of correctly classified examples. In a regression problem, the optimization metric may be mean squared error, whicFew readers

• Train quality

  You can select how much time and computing power to spend training the model; higher values are likely to provide more accurate predictions.Few readers

• Train/Test split

  Train-Test Split is a technique in machine learning to divide a dataset into two parts: training data and testing data. The purpose is to use the training data to build a model, and then test the model's accuracy on the testing data. For example, let's say you have a dataset of 1000 student exam scores. You could split it into a training set of 800 scores and a testing set of 200 scores. You would use the 800 scores to train a model, and then use the 200 scores to test the accuracy of the moFew readers

• Saturation point

  A numeric upper bound for the values a forecasting model will generateFew readers