AUTOMATIC MACHINE LEARNING MODEL GENERATION
A system may automatically generate a predictive machine learning model by automatically performing various processes based on an analysis of the data as well as metadata associated with the data. The system may accept a selection of data and a prediction field from the data. The system may automatically generate a set of features based on the data and may automatically remove certain features that cause inaccuracies in the model. The system may balance the data based on a representation rate of certain outcomes. The system may train and select a model based on several candidate models. The system may then perform the predictions based on the selected model and send an indication of the predictions to a user.
1 . A method for data processing, comprising:
receiving a selection of a dataset, a prediction field from the dataset, and a set of parameters associated with the prediction field;
processing the dataset based at least in part on sampling the dataset according to a sampling rate, removing a portion of the dataset, filtering out a set of outliers from the dataset, using metadata associated with the dataset to generate features for the dataset, and segmenting the dataset into a first subset of training data and a second subset of evaluation data;
training a plurality of candidate predictive machine learning models using the first subset of training data from the processed dataset and the set of parameters associated with the prediction field;
evaluating a predictive accuracy of the plurality of candidate predictive machine learning models using the second subset of evaluation data from the processed dataset;
transmitting an indication of the plurality of candidate predictive machine learning models and the predictive accuracy of the plurality of candidate predictive machine learning models;
receiving a selection of a first predictive machine learning model from the plurality of candidate predictive machine learning models; and
predicting one or more values for the prediction field using the first predictive machine learning model.
2 . The method of claim 1 , further comprising:
determining respective scores for the plurality of candidate predictive machine learning models based at least in part on performing a statistical analysis of the predictive accuracy of the plurality of candidate predictive machine learning models.
3 . The method of claim 2 , wherein the respective scores for the plurality of candidate predictive machine learning models are displayed on the user interface of the client device.
4 . The method of claim 1 , further comprising:
determining a correlation between the one or more values generated by the first predictive machine learning model and actual values from the dataset.
5 . The method of claim 4 , further comprising:
displaying the correlation.
6 . The method of claim 1 , wherein the metadata indicates respective data types and characteristics of fields in the dataset.
7 . The method of claim 1 , wherein the plurality of candidate predictive machine learning models are trained using parameters defined by a user of a client device.
8 . An apparatus for data processing, comprising:
a processor;
memory coupled with the processor; and
instructions stored in the memory and executable by the processor to cause the apparatus to:
receive a selection of a dataset, a prediction field from the dataset, and a set of parameters associated with the prediction field;
process the dataset based at least in part on sampling the dataset according to a sampling rate, removing a portion of the dataset, filtering out a set of outliers from the dataset, using metadata associated with the dataset to generate features for the dataset, and segmenting the dataset into a first subset of training data and a second subset of evaluation data;
train a plurality of candidate predictive machine learning models using the first subset of training data from the processed dataset and the set of parameters associated with the prediction field;
evaluate a predictive accuracy of the plurality of candidate predictive machine learning models using the second subset of evaluation data from the processed dataset;
transmit an indication of the plurality of candidate predictive machine learning models and the predictive accuracy of the plurality of candidate predictive machine learning models;
receive a selection of a first predictive machine learning model from the plurality of candidate predictive machine learning models; and
predict one or more values for the prediction field using the first predictive machine learning model.
9 . The apparatus of claim 8 , wherein the instructions are further executable by the processor to cause the apparatus to:
determine respective scores for the plurality of candidate predictive machine learning models based at least in part on performing a statistical analysis of the predictive accuracy of the plurality of candidate predictive machine learning models.
10 . The apparatus of claim 9 , wherein the respective scores for the plurality of candidate predictive machine learning models are displayed on the user interface of the client device.
11 . The apparatus of claim 8 , wherein the instructions are further executable by the processor to cause the apparatus to:
determine a correlation between the one or more values generated by the first predictive machine learning model and actual values from the dataset.
12 . The apparatus of claim 11 , wherein the instructions are further executable by the processor to cause the apparatus to:
display the correlation.
13 . The apparatus of claim 8 , wherein the metadata indicates respective data types and characteristics of fields in the dataset.
14 . The apparatus of claim 8 , wherein the plurality of candidate predictive machine learning models are trained using parameters defined by a user of a client device.
15 . A non-transitory computer-readable medium storing code for data processing, the code comprising instructions executable by a processor to:
receive a selection of a dataset, a prediction field from the dataset, and a set of parameters associated with the prediction field;
process the dataset based at least in part on sampling the dataset according to a sampling rate, removing a portion of the dataset, filtering out a set of outliers from the dataset, using metadata associated with the dataset to generate features for the dataset, and segmenting the dataset into a first subset of training data and a second subset of evaluation data;
train a plurality of candidate predictive machine learning models using the first subset of training data from the processed dataset and the set of parameters associated with the prediction field;
evaluate a predictive accuracy of the plurality of candidate predictive machine learning models using the second subset of evaluation data from the processed dataset;
transmit an indication of the plurality of candidate predictive machine learning models and the predictive accuracy of the plurality of candidate predictive machine learning models;
receive a selection of a first predictive machine learning model from the plurality of candidate predictive machine learning models; and
predict one or more values for the prediction field using the first predictive machine learning model.
16 . The non-transitory computer-readable medium of claim 15 , wherein the instructions are further executable by the processor to:
determine respective scores for the plurality of candidate predictive machine learning models based at least in part on performing a statistical analysis of the predictive accuracy of the plurality of candidate predictive machine learning models.
17 . The non-transitory computer-readable medium of claim 16 , wherein the respective scores for the plurality of candidate predictive machine learning models are displayed on the user interface of the client device.
18 . The non-transitory computer-readable medium of claim 15 , wherein the instructions are further executable by the processor to:
determine a correlation between the one or more values generated by the first predictive machine learning model and actual values from the dataset.
19 . The non-transitory computer-readable medium of claim 18 , wherein the instructions are further executable by the processor to:
display the correlation.
20 . The non-transitory computer-readable medium of claim 15 , wherein the metadata indicates respective data types and characteristics of fields in the dataset.