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Commit 72634f27 authored by Yanbo Liang's avatar Yanbo Liang Committed by Xiangrui Meng
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[MINOR][ML][DOC] Rename weights to coefficients in user guide

We should use ```coefficients``` rather than ```weights``` in user guide that freshman can get the right conventional name at the outset. mengxr vectorijk

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #9493 from yanboliang/docs-coefficients.
parent 77488fb8
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...@@ -71,8 +71,8 @@ val lr = new LogisticRegression() ...@@ -71,8 +71,8 @@ val lr = new LogisticRegression()
// Fit the model // Fit the model
val lrModel = lr.fit(training) val lrModel = lr.fit(training)
// Print the weights and intercept for logistic regression // Print the coefficients and intercept for logistic regression
println(s"Weights: ${lrModel.weights} Intercept: ${lrModel.intercept}") println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}")
{% endhighlight %} {% endhighlight %}
</div> </div>
...@@ -105,8 +105,8 @@ public class LogisticRegressionWithElasticNetExample { ...@@ -105,8 +105,8 @@ public class LogisticRegressionWithElasticNetExample {
// Fit the model // Fit the model
LogisticRegressionModel lrModel = lr.fit(training); LogisticRegressionModel lrModel = lr.fit(training);
// Print the weights and intercept for logistic regression // Print the coefficients and intercept for logistic regression
System.out.println("Weights: " + lrModel.weights() + " Intercept: " + lrModel.intercept()); System.out.println("Coefficients: " + lrModel.coefficients() + " Intercept: " + lrModel.intercept());
} }
} }
{% endhighlight %} {% endhighlight %}
...@@ -124,8 +124,8 @@ lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8) ...@@ -124,8 +124,8 @@ lr = LogisticRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
# Fit the model # Fit the model
lrModel = lr.fit(training) lrModel = lr.fit(training)
# Print the weights and intercept for logistic regression # Print the coefficients and intercept for logistic regression
print("Weights: " + str(lrModel.weights)) print("Coefficients: " + str(lrModel.coefficients))
print("Intercept: " + str(lrModel.intercept)) print("Intercept: " + str(lrModel.intercept))
{% endhighlight %} {% endhighlight %}
</div> </div>
...@@ -258,8 +258,8 @@ val lr = new LinearRegression() ...@@ -258,8 +258,8 @@ val lr = new LinearRegression()
// Fit the model // Fit the model
val lrModel = lr.fit(training) val lrModel = lr.fit(training)
// Print the weights and intercept for linear regression // Print the coefficients and intercept for linear regression
println(s"Weights: ${lrModel.weights} Intercept: ${lrModel.intercept}") println(s"Coefficients: ${lrModel.coefficients} Intercept: ${lrModel.intercept}")
// Summarize the model over the training set and print out some metrics // Summarize the model over the training set and print out some metrics
val trainingSummary = lrModel.summary val trainingSummary = lrModel.summary
...@@ -302,8 +302,8 @@ public class LinearRegressionWithElasticNetExample { ...@@ -302,8 +302,8 @@ public class LinearRegressionWithElasticNetExample {
// Fit the model // Fit the model
LinearRegressionModel lrModel = lr.fit(training); LinearRegressionModel lrModel = lr.fit(training);
// Print the weights and intercept for linear regression // Print the coefficients and intercept for linear regression
System.out.println("Weights: " + lrModel.weights() + " Intercept: " + lrModel.intercept()); System.out.println("Coefficients: " + lrModel.coefficients() + " Intercept: " + lrModel.intercept());
// Summarize the model over the training set and print out some metrics // Summarize the model over the training set and print out some metrics
LinearRegressionTrainingSummary trainingSummary = lrModel.summary(); LinearRegressionTrainingSummary trainingSummary = lrModel.summary();
...@@ -330,8 +330,8 @@ lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8) ...@@ -330,8 +330,8 @@ lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8)
# Fit the model # Fit the model
lrModel = lr.fit(training) lrModel = lr.fit(training)
# Print the weights and intercept for linear regression # Print the coefficients and intercept for linear regression
print("Weights: " + str(lrModel.weights)) print("Coefficients: " + str(lrModel.coefficients))
print("Intercept: " + str(lrModel.intercept)) print("Intercept: " + str(lrModel.intercept))
# Linear regression model summary is not yet supported in Python. # Linear regression model summary is not yet supported in Python.
......
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