Least Squares optimization
$begingroup$
The cost function given as $hat{beta} = (Y - beta X)^T (Y-beta X)$ is used to evaluate the weights $beta$. Here $X$ is the data and $Y$ is the output. On taking the derivative, we get the estimates of the weights. This is a Least Squares formulation.
1) Can Least Squares (LS) be used when the observation (outputs) $y_i$, $i=1,2,..,N$ number of examples are categorical? I don't quite get the picture how classification problems using LS works in terms of derivative for categorical cases.
2) Can LS be used when the data $X$ is a one-hot encoding? Would the formulation and derivative be the same?
classification statistics optimization machine-learning-model
asked 2 mins ago
Srishti MSrishti M
1696
$endgroup$
add a comment |
$begingroup$
The cost function given as $hat{beta} = (Y - beta X)^T (Y-beta X)$ is used to evaluate the weights $beta$. Here $X$ is the data and $Y$ is the output. On taking the derivative, we get the estimates of the weights. This is a Least Squares formulation.
1) Can Least Squares (LS) be used when the observation (outputs) $y_i$, $i=1,2,..,N$ number of examples are categorical? I don't quite get the picture how classification problems using LS works in terms of derivative for categorical cases.
2) Can LS be used when the data $X$ is a one-hot encoding? Would the formulation and derivative be the same?
classification statistics optimization machine-learning-model
asked 2 mins ago
Srishti MSrishti M
1696
$endgroup$
add a comment |
$begingroup$
The cost function given as $hat{beta} = (Y - beta X)^T (Y-beta X)$ is used to evaluate the weights $beta$. Here $X$ is the data and $Y$ is the output. On taking the derivative, we get the estimates of the weights. This is a Least Squares formulation.
1) Can Least Squares (LS) be used when the observation (outputs) $y_i$, $i=1,2,..,N$ number of examples are categorical? I don't quite get the picture how classification problems using LS works in terms of derivative for categorical cases.
2) Can LS be used when the data $X$ is a one-hot encoding? Would the formulation and derivative be the same?
classification statistics optimization machine-learning-model
asked 2 mins ago
Srishti MSrishti M
1696
$endgroup$
The cost function given as $hat{beta} = (Y - beta X)^T (Y-beta X)$ is used to evaluate the weights $beta$. Here $X$ is the data and $Y$ is the output. On taking the derivative, we get the estimates of the weights. This is a Least Squares formulation.
1) Can Least Squares (LS) be used when the observation (outputs) $y_i$, $i=1,2,..,N$ number of examples are categorical? I don't quite get the picture how classification problems using LS works in terms of derivative for categorical cases.
2) Can LS be used when the data $X$ is a one-hot encoding? Would the formulation and derivative be the same?
classification statistics optimization machine-learning-model
classification statistics optimization machine-learning-model
asked 2 mins ago
Srishti MSrishti M
1696
asked 2 mins ago
Srishti MSrishti M
1696
asked 2 mins ago
Srishti MSrishti M
1696
asked 2 mins ago
Srishti MSrishti M
1696
asked 2 mins ago
Srishti MSrishti M
1696
1696
add a comment |
add a comment |
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