Set more parameters as user-specified constants.
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@ -73,18 +73,21 @@ model_input["target"].value_counts()
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# %% jupyter={"outputs_hidden": false, "source_hidden": false}
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# %% jupyter={"outputs_hidden": false, "source_hidden": false}
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# bins = [-10, 0, 10] # bins for z-scored targets
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# bins = [-10, 0, 10] # bins for z-scored targets
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bins = [-1, 0, 4] # bins for stressfulness (0-4) target
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BINS = [-1, 0, 4] # bins for stressfulness (0-4) target
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print("BINS: ", BINS)
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model_input["target"], edges = pd.cut(
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model_input["target"], edges = pd.cut(
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model_input.target, bins=bins, labels=["low", "high"], retbins=True, right=True
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model_input.target, bins=BINS, labels=["low", "high"], retbins=True, right=True
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) # ['low', 'medium', 'high']
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) # ['low', 'medium', 'high']
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model_input["target"].value_counts(), edges
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print(model_input["target"].value_counts())
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model_input = model_input[model_input["target"] != "medium"]
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REMOVE_MEDIUM = True
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if ("medium" in model_input["target"]) and REMOVE_MEDIUM:
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model_input = model_input[model_input["target"] != "medium"]
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print(model_input["target"].value_counts())
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model_input["target"] = (
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model_input["target"] = (
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model_input["target"].astype(str).apply(lambda x: 0 if x == "low" else 1)
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model_input["target"].astype(str).apply(lambda x: 0 if x == "low" else 1)
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)
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)
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model_input["target"].value_counts()
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# %% jupyter={"outputs_hidden": false, "source_hidden": false}
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# %% jupyter={"outputs_hidden": false, "source_hidden": false}
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# UnderSampling
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# UnderSampling
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if UNDERSAMPLING:
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if UNDERSAMPLING:
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