Platform For AI:Overview of Designer components

Component type

Component

Description

Custom component

Custom Component

Creates a custom component in AI Asset Management. Use the custom component in Designer for model training alongside official components.

Source/Target

Read OSS Data

Reads files or folders from a specified path in an Object Storage Service (OSS) bucket.

Read CSV File

Reads CSV files from OSS, HTTP, or HDFS.

Read Table

Reads data from a MaxCompute table in the current project.

Write to Table

Writes upstream data to a MaxCompute table.

Custom script

SQL Script

Executes custom SQL statements in MaxCompute.

Python Script

Installs dependency packages and runs custom Python functions.

Tools

Register Dataset

Registers a dataset to AI Asset Management.

Register Model

Registers a model to AI Asset Management.

Update EAS Service (Beta)

Calls eascmd to update a specified Elastic Algorithm Service (EAS) service. The service must be in a running state. Each update creates a new service version.

LLM data processing

Data conversion

Export MaxCompute Table to OSS

Exports a MaxCompute table to OSS.

Import OSS Data to MaxCompute Table

Imports data from OSS to a MaxCompute table.

LLM data processing (DLC)

LLM-MD5 Deduplication (DLC)

Calculates the MD5 hash of text content and removes duplicate entries.

LLM-Text Normalization (DLC)

Performs Unicode normalization on text and converts Traditional Chinese characters to Simplified Chinese.

LLM-Special Content Removal (DLC)

Removes URLs and strips HTML formatting to extract plain text.

LLM-Special Character Ratio Filter (DLC)

Filters samples based on the ratio of special characters to total text length.

LLM-Copyright Information Removal (DLC)

Removes copyright information from text, such as comments in code file headers.

LLM-Count Filter (DLC)

Filters samples based on the ratio of numeric and alphabetic characters to total text length.

LLM-Length Filter (DLC)

Filters samples based on total text length, average line length, and maximum line length.

LLM-Text Quality Scoring and Language Identification - FastText (DLC)

Identifies the language of a text, calculates a quality score, and filters samples based on the specified language and score range.

LLM-Sensitive Word Filter (DLC)

Filters samples that contain words from a specified sensitive word dictionary.

LLM-Sensitive Information Masking (DLC)

Masks sensitive information, such as email addresses, phone numbers, and ID numbers.

LLM-Document Similarity Deduplication (DLC)

Deduplicates documents by calculating SimHash similarity scores.

LLM-N-Gram Repetition Ratio Filter (DLC)

Filters samples based on the character-level or word-level N-gram repetition ratio.

LLM-Expand LaTeX Macro Definition (DLC)

Used for data in the TEX document format. It performs inline expansion of all macros that have no parameters. If a macro consists of letters and numbers and has no parameters, the macro name is replaced with its value.

LLM-Remove LaTeX Bibliography (DLC)

Removes the bibliography section from a LaTeX document.

LLM-Remove LaTeX Comment Lines (DLC)

Removes comment lines and inline comments from LaTeX source text.

LLM-Remove LaTeX Document Header (DLC)

Used for data in the TEX document format. It finds the first string that matches the <section-type>[optional-args]{name} chapter format, removes all content before it, and retains all content after the first matched chapter, including the chapter title.

LLM data processing (MaxCompute)

LLM-MD5 Deduplication (MaxCompute)

Calculates the MD5 hash of text content and removes duplicate entries.

LLM-Text Normalization (MaxCompute)

Performs Unicode normalization and converts Traditional Chinese characters to Simplified Chinese.

LLM-Special Content Removal (MaxCompute)

Removes content such as navigation, author information, URLs, and HTML formatting.

LLM-Special Character Ratio Filter (MaxCompute)

Filters samples based on the ratio of special characters to total text length.

LLM-Copyright Information Removal (MaxCompute)

Removes copyright information from text, such as comments in code file headers.

LLM-Count Filter (MaxCompute)

Filters samples based on the count of letters, numbers, and delimiters.

LLM-Length Filter (MaxCompute)

Filters samples based on total text length, average line length, and maximum line length.

LLM-Text Quality Scoring and Language Identification (MaxCompute)

Identifies the language of a text, calculates a quality score, and filters samples based on the specified language and score range.

LLM-Sensitive Word Filter (MaxCompute)

Filters samples that contain words from a specified sensitive word dictionary.

LLM-Sensitive Information Masking (MaxCompute)

Masks sensitive information, such as email addresses, phone numbers, and ID numbers.

LLM-N-Gram Repetition Ratio Filter (MaxCompute)

Filters samples based on the character-level or word-level N-gram repetition ratio.

LLM-Expand LaTeX Macro Definition (MaxCompute)

Inlines parameter-less macro definitions in TEX-formatted data.

LLM-Remove LaTeX Bibliography (MaxCompute)

Removes the bibliography section from a LaTeX document.

LLM-Remove LaTeX Comment Lines (MaxCompute)

Removes comment lines and inline comments from LaTeX source text.

LLM-Remove LaTeX Document Header (MaxCompute)

Removes all content preceding the first section declaration in a LaTeX document.

LVM data processing (DLC)

Video preprocessing operators

LVM-Text Area Filter (DLC)

Filters video data based on the quantity of text present in the frames.

LVM-Motion Filter (DLC)

Filters video data based on a specified range of motion speed.

LVM-Aesthetics Filter (DLC)

Filters video data that falls below a specified aesthetic quality score.

LVM-Aspect Ratio Filter (DLC)

Filters video data based on a specified range of aspect ratios.

LVM-Duration Filter (DLC)

Filters video data based on a specified range of durations.

LVM-Video-Text Similarity Filter (DLC)

Filters video data based on the semantic similarity score between the video and its associated text.

LVM-Compliance Filter (DLC)

Filters video data based on its Not Safe For Work (NSFW) score.

LVM-Resolution Filter (DLC)

Filters video data based on a specified range of resolutions.

LVM-Watermark Filter (DLC)

Filters video data that contains watermarks.

LVM-Tag Filter (DLC)

Filters video data that does not match a specified set of tags.

LVM-Tag Generation (DLC)

Generates descriptive tags for video frames.

LVM-Frame Text Generation (DLC)

Generates descriptive text for video frames.

LVM-Video Text Generation (DLC)

Generates descriptive text for entire videos.

Image preprocessing operators

LVM-Image Aesthetics Filter (DLC)

Filters image data that falls below a specified aesthetic quality score.

LVM-Image Aspect Ratio Filter (DLC)

Filters image data based on a specified range of aspect ratios.

LVM-Image Face Ratio Filter (DLC)

Filters image data based on the ratio of face area to total image area.

LVM-Image Compliance Filter (DLC)

Filters image data based on its Not Safe For Work (NSFW) score.

LVM-Image Resolution Filter (DLC)

Filters image data based on a specified range of resolutions.

LVM-Image Size Filter (DLC)

Filters image data based on a specified range of file sizes.

LVM-Image-Text Matching Filter (DLC)

Filters image-text pairs based on their matching score.

LVM-Image-Text Similarity Filter (DLC)

Filters image-text pairs based on their semantic similarity score.

LVM-Image Watermark Filter (DLC)

Filters image data that contains watermarks.

LVM-Image Captioning (DLC)

Generates natural language descriptions for images.

LLM training and inference

BERT Model Offline Inference

Performs offline inference using a pre-trained BERT classification model to classify text in an input table.

Component type

Component

Description

Data preprocessing

Random Sampling

Performs random, independent sampling on input data based on a specified ratio or count.

Weighted Sampling

Generates a sample from the input data using a weighted selection method.

Filter and Map

Filters data rows based on a SQL expression and renames output columns.

Stratified Sampling

Divides data into groups based on a specified column and performs random sampling within each group.

JOIN

Combines two tables based on a join key, similar to a SQL JOIN statement.

Merge Columns

Merges columns from two tables. Both tables must have the same number of rows.

Merge Rows (UNION)

Appends rows from two tables. Both tables must have the same number and type of selected columns.

Type Transformation

Converts data types of specified columns to String, Double, or Integer. Fills missing values on conversion failure.

Add ID Column

Adds a sequential numeric ID column as the first column of the table.

Split

Randomly splits a dataset into two subsets, typically for creating training and testing sets.

Fill Missing Values

Fills missing values in specified columns using a selected method, such as mean, median, mode, or a custom value.

Normalization

Rescales numerical features to a common range, such as [0, 1]. Supports both dense and sparse data formats.

Standardization

Rescales features to have a mean of 0 and a standard deviation of 1 (z-score normalization).

KV to Table

Converts a table from a sparse Key-Value (KV) format to a dense table format.

Table to KV

Converts a dense table to a sparse Key-Value (KV) format.

Feature engineering

Feature Importance Filtering

Filters for the top N features based on importance scores generated by other components.

Principal Component Analysis

Performs Principal Component Analysis (PCA) to reduce the dimensionality of a dataset by transforming features into a set of linearly uncorrelated principal components.

Feature Scaling

Applies min-max, log, or z-score scaling transformations to numerical features.

Feature Discretization

Converts continuous numerical features into discrete categorical features (bins).

Feature Anomaly Smoothing

Clamps anomalous feature values to a specified range. Supports both sparse and dense data formats.

Singular Value Decomposition

Performs Singular Value Decomposition (SVD) on a matrix.

Anomaly Detection

Detects outliers in data containing both continuous and categorical features.

Linear Model Feature Importance

Calculates feature importance scores using a linear regression or logistic regression model.

Discrete Feature Analysis

Analyzes the statistical distribution of discrete features.

Random Forest Feature Importance Evaluation

Calculates feature importance scores using a trained Random Forest model.

Filter-based Feature Selection

Selects a subset of features using filter methods such as Chi-squared, Gini index, or Information Gain.

Feature Encoding

Encodes non-linear features into linear features using a Gradient Boosting Decision Tree (GBDT) model.

One-Hot Encoding

Converts categorical features into a binary vector representation. The output is in a sparse Key-Value (KV) format.

Statistical analysis

Data View

Provides a visual summary of data distribution and statistics for selected columns.

Covariance

Calculates the covariance between two random variables to measure how they change together.

Empirical Probability Density Plot

Generates a probability density plot using either empirical distribution or kernel density estimation.

Full Table Statistics

Calculates descriptive statistics for all columns or a subset of columns in a table.

Chi-Square Goodness-of-Fit Test

Used for categorical variables. It tests whether the actual observed frequencies and theoretical frequencies are consistent across the categories of a single multinomial categorical variable. The null hypothesis is that there is no difference between the observed and theoretical frequencies.

Box Plot

A box plot chart is a statistical graph used to display the dispersion of a dataset. It is mainly used to reflect the characteristics of the raw data distribution and can also be used to compare the distribution characteristics of multiple datasets.

Scatter Plot

A scatter chart is a distribution plot of data points on a Cartesian coordinate system in regression analysis.

Correlation Matrix

The correlation coefficient algorithm calculates the correlation coefficient between each column in a matrix. The value ranges from [-1,1]. During calculation, the count is based on the number of elements that are not empty in both columns. The count may vary between different pairs of columns.

Two-Sample T-Test

Based on statistical principles, this component tests whether there is a significant difference between the means of two samples.

One-Sample T-Test

Tests whether there is a significant difference between the population mean of a variable and a specified value. The tested sample must follow a normal distribution.

Normality Test

Uses observed values to determine whether a population follows a normal distribution. It is an important type of special goodness-of-fit hypothesis test in statistical decision-making.

Lorenz Curve

Visually displays the income distribution of a country or region.

Percentile

A statistical term used to calculate the percentile of data in a table column.

Pearson Coefficient

A linear correlation coefficient that reflects the degree of linear correlation between two variables.

Histogram

A histogram, also known as a mass distribution chart, is a statistical report graph that uses a series of vertical bars or line segments of varying heights to represent data distribution.

Machine learning

Prediction

The inputs are a trained model and prediction data, and the output is the prediction result.

XGBoost Training

This algorithm extends and upgrades the boosting algorithm. It is easy to use and robust, and is widely used in various machine learning production systems and competitions. It currently supports classification and regression.

XGBoost Prediction

This algorithm extends and upgrades the boosting algorithm. It is easy to use and robust, and is widely used in various machine learning production systems and competitions. It currently supports classification and regression.

Linear Support Vector Machine

A machine learning method based on statistical learning theory. It improves the generalization ability of the learning machine by minimizing structural risk, thereby minimizing empirical risk and confidence range.

Logistic Regression for Binary Classification

A binary classification algorithm that supports both sparse and dense data formats.

GBDT for Binary Classification

This component works by setting a threshold. If a feature value is greater than the threshold, it is a positive sample. Otherwise, it is a negative sample.

PS-SMART for Binary Classification

Parameter Server (PS) is designed to handle large-scale offline and online training tasks. Scalable Multiple Additive Regression Tree (SMART) is an iterative algorithm that is based on Gradient Boosting Decision Tree (GBDT) and implemented on PS.

PS-based Logistic Regression for Binary Classification

A classic binary classification algorithm widely used in advertising and search scenarios.

PS-SMART for Multiclass Classification

Parameter Server (PS) is designed to handle large-scale offline and online training tasks. Scalable Multiple Additive Regression Tree (SMART) is an iterative algorithm that is based on Gradient Boosting Decision Tree (GBDT) and implemented on PS.

K-Nearest Neighbors

For each row of data in the prediction table, this component selects the K records with the closest distance from the training table. The class with the highest frequency among these K records is assigned as the class for that row.

Logistic Regression for Multiclass Classification

A binary classification algorithm. The logistic regression models provided by PAI support multiclass classification and both sparse and dense data formats.

Random Forests

A classifier that includes multiple decision trees. Its classification result is determined by the mode of the classes output by individual trees.

Naive Bayes

A probabilistic classification algorithm based on Bayes' theorem with an independence assumption.

K-Means Clustering

First, this component randomly selects K objects as the initial cluster centers for each cluster. Then, it calculates the distance between the remaining objects and each cluster center, assigns them to the nearest cluster, and recalculates the cluster center for each cluster.

DBSCAN

Use the DBSCAN component to build clustering models.

GMM Training

Use the GMM Training component to implement model classification.

DBSCAN Prediction

Use the DBSCAN Prediction component to predict the cluster to which new data points belong based on a DBSCAN training model.

GMM Prediction

Use the GMM Prediction component to perform clustering prediction based on a trained Gaussian mixture model.

GBDT Regression

An iterative decision tree algorithm suitable for linear and non-linear regression scenarios.

Linear Regression

A model that analyzes the linear relationship between a dependent variable and multiple independent variables.

PS-SMART Regression

This component is designed to handle large-scale offline and online training tasks. SMART is an iterative algorithm that is based on GBDT and implemented on PS.

PS Linear Regression

A model that analyzes the linear relationship between a dependent variable and multiple independent variables. Parameter Server (PS) is designed to handle large-scale offline and online training tasks.

Binary Classification Evaluation

Calculates metrics such as AUC, KS, and F1-score, and outputs KS curves, PR curves, ROC curves, LIFT charts, and Gain charts.

Regression Model Evaluation

Evaluates the quality of regression algorithm models based on prediction results and raw results, and outputs evaluation metrics and a residual histogram.

Clustering Model Evaluation

Evaluates the quality of clustering models based on raw data and clustering results, and outputs evaluation metrics.

Confusion Matrix

Suitable for supervised learning and corresponds to the matching matrix in unsupervised learning.

Multiclass Classification Evaluation

Evaluates the quality of multiclass classification algorithm models based on the prediction results and raw results of classification models, and outputs evaluation metrics such as Accuracy, Kappa, and F1-Score.

Deep learning

Deep learning frameworks and activation instructions

PAI supports deep learning frameworks. Use these frameworks and hardware resources to run deep learning algorithms.

Time series

x13_arima

An Arima algorithm for seasonal adjustment that is encapsulated based on the open source X-13ARIMA-SEATS.

x13_auto_arima

Includes an automatic ARIMA model selection program, which is mainly based on the program by Gomez and Maravall (1998) implemented in TRMO (1996) and subsequent revisions.

Prophet

Performs Prophet time series prediction on each row of MTable data and provides prediction results for the next time period.

MTable Assembler

Aggregates a table into an MTable based on grouping columns.

MTable Expander

Expands an MTable into a table.

Recommendation methods

FM Algorithm

The Factorization Machine (FM) algorithm considers the interactions between features. It is a non-linear model suitable for recommendation scenarios in e-commerce, advertising, and live streaming.

ALS Matrix Factorization

The Alternating Least Squares (ALS) algorithm performs model decomposition on a sparse matrix and evaluates the values of missing items to obtain a basic training model.

Swing Training

An item recall algorithm. Use the Swing Training component to measure item similarity based on the User-Item-User principle.

Swing Recommendation

A batch processing prediction component for Swing. Use this component to perform offline prediction based on a Swing training model and prediction data.

Collaborative Filtering (etrec)

etrec is an item-based collaborative filtering algorithm. The input consists of two columns, and the output is the top N most similar items.

Vector-based Recall Evaluation

Calculates the hit rate of recalls. The hit rate is used to evaluate the quality of the results. A higher hit rate indicates that the vectors produced by training achieve more accurate recall results.

Anomaly detection

Local Outlier Factor Anomaly Detection

Determines whether a sample is an anomaly based on its Local Outlier Factor (LOF) value.

IForest Anomaly Detection

Uses a sub-sampling algorithm to reduce computational complexity. It can identify anomalies in data and has significant application effects in anomaly detection.

One-Class SVM Anomaly Detection

Different from traditional SVM, this is an unsupervised learning algorithm. Use One-Class SVM Anomaly Detection to predict anomalies by learning a boundary.

Natural Language Processing

Text Summarization Prediction

Extracts, refines, or summarizes key information from lengthy and repetitive text sequences. News headline summarization is a special case of text summarization. Use the Text Summarization Prediction component to call a specified pre-trained model to predict news text and generate news headlines.

Machine Reading Comprehension Prediction

Performs offline prediction with the generated machine reading comprehension training model.

Text Summarization Training

Extracts, refines, or summarizes key information from lengthy and repetitive text sequences. News headline summarization is a special case of text summarization. Use the Text Summarization Training component to train a model that generates news headlines to summarize the central ideas and key information of news articles.

Machine Reading Comprehension Training

Trains a machine reading comprehension model that can quickly understand and answer questions based on a given document.

Split Word

Based on the AliWS (Alibaba Word Segmenter) lexical analysis system, this component performs tokenization on the content of a specified column. The resulting tokens are separated by spaces.

Trituple to KV

Converts a trituple table (row,col,value) to a key-value (KV) table (row,[col_id:value]).

String Similarity

A basic operation in machine learning, mainly used in information retrieval, natural language processing, and bioinformatics.

String Similarity-Top N

Calculates string similarity and filters out the top N most similar data.

Stop Word Filter

A pre-processing method in text analysis used to filter noise (such as "the", "is", or "a") from tokenization results.

ngram-count

A step in language model training. It generates n-grams based on words and counts the occurrences of each n-gram across the entire corpus.

Text Summarization

A simple and coherent short text that comprehensively and accurately reflects the central idea of a document. Automatic summarization uses a computer to automatically extract summary content from the original document.

Keyword Extraction

An important technique in natural language processing. It extracts words from a text that are highly relevant to the meaning of the document.

Sentence Splitting

Splits a piece of text into sentences based on punctuation. This component is mainly used for pre-processing before text summarization, converting a paragraph into a one-sentence-per-line format.

Semantic Vector Distance

Based on semantic vector results from algorithms (such as word embeddings generated by Word2Vec), this component calculates extension words (or sentences) for given words (or sentences) by finding the set of vectors with the closest distance. One use case is to return a list of the most similar words based on the input word and the word embeddings generated by Word2Vec.

Doc2Vec

Use the Doc2Vec algorithm component to map documents to vectors. The input is a vocabulary, and the output is a document vector table, a word vector table, or a vocabulary.

Conditional Random Field

A conditional random field (CRF) is a probabilistic distribution model of a set of output random variables given a set of input random variables. Its characteristic is the assumption that the output random variables form a Markov random field.

Document Similarity

Builds on string similarity to calculate the similarity between pairs of documents or sentences based on words.

PMI

This algorithm counts the co-occurrence of all words in several documents and calculates the pointwise mutual information (PMI) between each pair.

Conditional Random Field Prediction

An algorithm component based on the linearCRF online prediction model, mainly used for sequence labeling problems.

Split Word (Generate Model)

Based on the AliWS (Alibaba Word Segmenter) lexical analysis system, this component generates a tokenization model based on parameters and a custom dictionary.

Word Count

Takes strings as input (entered manually or read from a file) and uses a program to count the total number of words and the frequency of each word.

TF-IDF

A common weighting technique for information retrieval and text mining. It is often used in search engines as a measure or rating of the relevance between a document and a user query.

PLDA

In PAI, you can set the topic parameter for the PLDA component to abstract different topics from each document.

Word2Vec

The Word2Vec algorithm component uses a neural network to map words to vectors in a K-dimensional space through training. It supports operations on the vectors that represent words, corresponding to their semantics. The input is a word column or a vocabulary, and the output is a word vector table and a vocabulary.

Network analysis

Tree Depth

Outputs the depth and tree ID of each node.

k-Core

Finds closely connected subgraph structures in a graph that meet a specified coreness. The maximum core number of a node is called the core number of the graph.

Single-Source Shortest Path

Uses the Dijkstra algorithm. Given a starting point, it outputs the shortest path from that point to all other nodes.

PageRank

Originated from web search ranking. It uses the link structure of web pages to calculate the rank of each page.

Label Propagation Clustering

The Label Propagation Algorithm (LPA) is a graph-based semi-supervised learning method. The basic idea is that a node's label (community) depends on the label information of its adjacent nodes. The degree of influence is determined by node similarity, and stability is achieved through iterative propagation.

Label Propagation Classification

A semi-supervised classification algorithm that uses the label information of labeled nodes to predict the labels of unlabeled nodes.

Modularity

A metric for evaluating community network structures. It assesses the tightness of communities within a network structure. A value above 0.3 usually indicates a clear community structure.

Maximal Connected Subgraph

In an undirected graph G, if a path connects vertex A to vertex B, A and B are connected. If a graph G contains several subgraphs where all vertices within each subgraph are connected, but no vertices between different subgraphs are connected, these subgraphs are called maximal connected subgraphs.

Vertex Clustering Coefficient

In an undirected graph G, this component calculates the density around each node. The density of a star network is 0, and the density of a fully connected network is 1.

Edge Clustering Coefficient

In an undirected graph G, this algorithm calculates the density around each edge.

Count Triangles

In an undirected graph G, this component outputs all triangles.

Finance

Data Transformation Module

Use this component to perform normalization, discretization, indexing, or Weight of Evidence (WOE) transformation on data.

Scorecard Training

A common modeling tool in credit risk assessment. It discretizes original variables by binning the input and then uses a linear model, such as logistic regression or linear regression, for model training. It includes features such as feature selection and score transformation.

Scorecard Prediction

Scores raw data based on the model results produced by the Scorecard Training component.

Binning

Performs feature discretization by segmenting continuous data into multiple discrete intervals. The Binning component supports equal frequency binning, equal width binning, and automatic binning.

Population Stability Index (PSI)

An important indicator for measuring the shift caused by sample changes. It is commonly used to measure the stability of samples.

Visual algorithms

Image Classification Training (torch)

If your business scenario involves image classification, use the Image Classification Training (torch) component to build an image classification model for model inference.

Video Classification Training

Use the Video Classification Training algorithm component to train a model and obtain a video classification model for inference.

Image Detection Training (easycv)

Builds an object detection model to detect and frame high-risk entities in images.

Image Self-Supervised Training

Directly trains raw, unlabeled images to obtain a model for image feature extraction.

Image Metric Learning Training (raw)

Builds a metric learning model for model inference.

Image Keypoint Training

If your business scenario involves human-related keypoint detection, use the Image Keypoint Training component to build a keypoint model for model inference.

Model Quantization

Provides mainstream model quantization algorithms. Use model quantization to compress and accelerate models for high-performance inference.

Model Pruning

Provides the mainstream model pruning algorithm AGP (taylorfo). Use model pruning to compress and accelerate models for high-performance inference.

Tools

Offline Model (OfflineModel) related components

A data structure stored in MaxCompute. Models generated by traditional machine learning algorithms based on the PAICommand framework are stored in the offline model format in the corresponding MaxCompute project. Use Offline Model related components to get offline models for offline prediction.

General-Purpose Model Export

Use the General-Purpose Model Export component to export a model trained in MaxCompute to a specified OSS path.

Custom scripts

PyAlink Script

Calls Alink's algorithms for classification, regression, and recommendation. The PyAlink script also seamlessly integrates with other Designer algorithm components to build and validate business traces.

Time Window SQL Script

Adds a multi-date loop execution feature to the standard SQL Script component. It is used for the parallel execution of daily SQL tasks within a specific time period.

Beta components

Lasso Regression Training

A compression estimation algorithm.

Lasso Regression Prediction

Supports both sparse and dense data formats. Use this component to predict numeric variables, such as loan amounts and temperatures.

Ridge Regression Prediction

Predicts numeric variables, including housing prices, sales volumes, and humidity.

Ridge Regression Training

The most commonly used regularization method for regression analysis of ill-posed problems.