Update my_pages/rashomon_developer.py
Browse files
my_pages/rashomon_developer.py
CHANGED
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@@ -13,14 +13,14 @@ def render():
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add_instruction_text(
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"""
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Consider the following data about individuals who did (green) or didn't (red) repay their loans. <br>
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Make
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"""
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)
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#### Choosing regularization
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st.markdown("""
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**Regularization:** Regularization is a technique that prevents the model from “overfitting,” meaning stop them from learning the noise or small quirks in the data.
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Choose a regularization technique:
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- L1 Regularization: This forces the model to work with as few features as possible, which helps highlight the most important signals and ignore irrelevant ones.
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- L2 Regularization: This forces the model to rely less on each feature, even though all features are used, which helps prevent any single feature from overpowering the model.
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@@ -49,12 +49,12 @@ def render():
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st.rerun()
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#### Choosing random seed
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st.markdown("""
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**Random Seed:** Random seed controls the stochasticity (or randomness) of the learning process.<br>
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Choose a random seed.
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"""
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)
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if regularization_method=="l1":
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random_seed = None
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if "random_seed" in st.session_state:
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random_seed = st.session_state.random_seed
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@@ -95,6 +95,6 @@ def render():
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Your choices during model development lead you to this model.
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"""
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st.markdown(
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f"<div style='text-align:center; color:#c0392b; font-size:20px;
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unsafe_allow_html=True,
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)
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add_instruction_text(
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"""
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Consider the following data about individuals who did (green) or didn't (red) repay their loans. <br>
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Make development choices now that will lead to the same variety of models as before.
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"""
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)
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#### Choosing regularization
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st.markdown("""
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**Regularization:** Regularization is a technique that prevents the model from “overfitting,” meaning stop them from learning the noise or small quirks in the data.
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+
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Choose a regularization technique:
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- L1 Regularization: This forces the model to work with as few features as possible, which helps highlight the most important signals and ignore irrelevant ones.
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- L2 Regularization: This forces the model to rely less on each feature, even though all features are used, which helps prevent any single feature from overpowering the model.
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st.rerun()
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#### Choosing random seed
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if regularization_method=="l1":
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st.markdown("""
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**Random Seed:** Random seed controls the stochasticity (or randomness) of the learning process.<br>
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Choose a random seed.
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"""
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)
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random_seed = None
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if "random_seed" in st.session_state:
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random_seed = st.session_state.random_seed
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Your choices during model development lead you to this model.
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"""
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st.markdown(
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f"<div style='text-align:center; color:#c0392b; font-size:20px;'>{multiplicity_message}</div>",
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unsafe_allow_html=True,
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)
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