Automated Machine Learning in Zoho Analytics

Automated machine learning in Zoho Analytics provides a code-free experience to train, verify, and build custom machine learning models with high efficiency. The simple and user-friendly design makes it possible for people with varied data-level expertise to create ML models easily and make smart business choices.

Building an ML model in Zoho Analytics comprises two simple steps,

  • Selecting the input data for training and the best fitting model for the use case
  • Assess the model performance and deploy the models to a new dataset for arriving at conclusions.

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Best Practices

Clearly define the business problem for which you seek to build an ML model. Meticulously choose the relevant tables or columns and exclude columns that do not affect the results. While auto-ML can be a powerful tool, its effectiveness depends on the specific needs and goals.

How to Launch an Auto ML Model in Zoho Analytics

Selecting the Data for training

  1. Select the Prediction Type that should be applied. Refer to the Predictive models sections for more information.Once you have decided the use case for which you need to build an ML model, click the Create New icon on the side navigation panel.
  2. Choose Auto ML from the drop-down menu.
  3. Give a suitable Analysis Name and Description.
  4. Select the Prediction Type that should be applied. Refer to the Predictive models sections for more information.
  5. Select the Training Table and the Target Column.
  6. Click Create.
Note: Currently columns of Date data type cannot used be used for model training.

Model Information

Once the training is completed, the model will be saved and will be listed in the Analysis tab along with other models. Click the model name to get an overview of the performance like the Algorithm Name, Accuracy and Training Time of the model.

Deploying the ML Model

Once you have assessed the quality and fine-tuned the ML model, you can deploy it on a production dataset to which the trained model should be deployed.

  1. Click the Deploy Now button on the top.
  2. Select the Input table for which the prediction model should be applied.
  3. Select the Output table - the trained model.
  4. Select the Schedule Time.
  5. Click Deploy Now.

A new table will be created; you can then create visualizations on top of it.

Machine Learning Models in Zoho Analytics

Zoho Analytics offers a wide array of machine learning models for automating various aspects of the machine learning process, including data preprocessing, feature engineering, model selection, hyper parameter tuning, and model evaluation.

Regression Model

Regression is a supervised learning method used to determine the relationship between the dependent and independent variables. The regression model is primarily used for predictive analysis.

Random Forest Regression

Random forest regression is a supervised machine learning algorithm that uses a combination (ensemble) of decision trees for prediction. Random subsets of the training data are chosen while constructing each decision tree. Each decision tree is combined to output a single prediction value.


The random forest model is best suited for predicting continuous values, like time series forecasting and price predictions. Since the algorithm involves constructing multiple decision trees, the predictions are always of high accuracy.

Classification Model 

Classification is a supervised machine learning method that predicts the category or type to which an observation or data point belongs. For instance, the classification of emails as spam, social, or primary. 

Random Forest Classification

The random forest classification is a supervised machine learning method that combines multiple decision trees to arrive at a conclusion. This method is best suited for discrete variables.

Clustering Model

Clustering is an unsupervised learning technique. This model identifies patterns and relationships within the data that are not immediately apparent and groups similar data points into clusters.

K - means

The K-means algorithm segregates a dataset into K distinct, non-overlapping clusters. This is an iterative process that assigns each data point to one of the K-clusters based on the input provided. This algorithm works effectively with quantitative data as it is based on calculating distances between data points.

K - modes

The K-modes algorithm is used for grouping categorical data, like segmentation based on demographics. Each cluster is determined by modes; the most frequent value in the cluster.

K - Prototypes

The K-Prototypes algorithm is an extension of the K-Means algorithm used for clustering datasets containing both numerical and categorical features. It combines the K-Means algorithm's clustering approach for numerical data with a mechanism to handle categorical data.