"In this tutorial, we will use the `tf.estimator` API in TensorFlow to solve a\n",
"In this tutorial, we will use the `tf.estimator` API in TensorFlow to solve a\n",
"binary classification problem: Given census data about a person such as age,\n",
"standard benchmark binary classification problem: Given census data about a \n",
"education, marital status, and occupation (the features), we will try to predict\n",
"person such as age, education, marital status, and occupation (the features),\n",
"whether or not the person earns more than 50,000 dollars a year (the target\n",
"we will try to predict whether or not the person earns more than 50,000 dollars\n",
"label). We will train a **logistic regression** model, and given an individual's\n",
"a year (the target label). We will train a **logistic regression** model, and given \n",
"information our model will output a number between 0 and 1, which can be\n",
"an individual's information our model will output a number between 0 and 1, which\n",
"interpreted as the probability that the individual has an annual income of over\n",
"can be interpreted as the probability that the individual has an annual income of over\n",
"50,000 dollars.\n",
"50,000 dollars.\n",
"\n",
"\n",
"Key Point: As a modeler and developer, think about how this data is used and the potential benefits and harm a model's predictions can cause. A model like this could reinforce societal biases and disparities. Is each feature relevant to the problem you want to solve or will it introduce bias? For more information, read about [ML fairness](https://developers.google.com/machine-learning/fairness-overview/).\n",
"\n",
"## Setup\n",
"## Setup\n",
"\n",
"\n",
"To try the code for this tutorial:\n",
"To try the code for this tutorial:\n",
...
@@ -316,16 +318,6 @@
...
@@ -316,16 +318,6 @@
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"execution_count": 0,
"outputs": []
"outputs": []
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{
"metadata": {
"id": "mLUJpWKoeCAE",
"colab_type": "text"
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"cell_type": "markdown",
"source": [
"Key Point: As a modeler and developer, think about how this data is used and the potential benefits and harm a model's predictions can cause. A model like this could reinforce societal biases and disparities. Is a feature relevant to the problem you want to solve or will it introduce bias? For more information, read about [ML fairness](https://developers.google.com/machine-learning/fairness-overview/)."
